TYDI QA: A Benchmark for Information-Seeking Question Answering

TYDI QA: A Benchmark for Information-Seeking Question Answering
in Typologically Diverse Languages

Jonathan H. Clark␆␅ Eunsol Choi␅ Michael Collins␆ Dan Garrette␆

Tom Kwiatkowski␆ Vitaly Nikolaev␄␃

Jennimaria Palomaki␄␃

Google Research
tydiqa@google.com

Abstrakt

Confidently making progress on multilingual
modeling requires challenging,
trustworthy
evaluations. We present TYDI QA—a question
answering dataset covering 11 typologically
diverse languages with 204K question-answer
pairs. The languages of TYDI QA are diverse
with regard to their typology—the set of
linguistic features each language expresses—-
such that we expect models performing well
on this set to generalize across a large num-
ber of the world’s languages. We present a
quantitative analysis of the data quality and
example-level qualitative linguistic analyses
of observed language phenomena that would
not be found in English-only corpora. To pro-
vide a realistic information-seeking task and
avoid priming effects, questions are written
by people who want to know the answer, Aber
don’t know the answer yet, and the data is
collected directly in each language without the
use of translation.

1 Einführung

When faced with a genuine information need,
everyday users now benefit from the help of
automatic question answering (QA) systems on
a daily basis with high-quality systems integrated
into search engines and digital assistants. Ihre
questions are information-seeking—they want to
know the answer, but don’t know the answer yet.
Recognizing the need to align research with the
impact it will have on real users, die Gemeinde
has responded with datasets of
Information-
seeking questions such as WikiQA (Yang et al.,
2015), MS MARCO (Nguyen et al., 2016), QuAC

Pronounced tie dye Q. A.—like the colorful t-shirt.
␆Project design ␅Modeling ␄Linguistic analysis ␃Data quality.

454

(Choi et al., 2018), and the Natural Questions
(NQ) (Kwiatkowski et al., 2019).

Jedoch, many people who might benefit from
QA systems do not speak English. The lan-
guages of the world exhibit an astonishing breadth
of linguistic phenomena used to express mean-
ing;
the World Atlas of Language Structures
(Comrie and Gil, 2005; Dryer and Haspelmath,
2013) categorizes over 2,600 languages1 by 192
typological features including phenomena such
as word order, reduplication, grammatical mean-
ings encoded in morphosyntax, case markings,
plurality systems, question marking, relativiza-
tion, and many more. If our goal is to build
models that can accurately represent all human
languages, we must evaluate these models on data
that exemplifies this variety.

(expensive) parallel

In addition to these typological distinctions,
modeling challenges arise due to differences in the
availability of monolingual data, the availability
von
translation data, Wie
standardized the writing system is variable spacing
conventions (z.B., Thai), and more. With these
needs in mind, we present the first public large-
scale multilingual corpus of information-seeking
question-answer pairs—using a simple-yet-novel
data collection procedure that is model-free and
translation-free. Our goals in doing so are:

1. to enable research progress toward building
high-quality question answering systems in
roughly the world’s top 100 languages;2 Und

2. to encourage research on models that behave
well across the linguistic phenomena and data
scenarios of the world’s languages.

We describe the typological features of TYDI
QA’s languages and provide glossed examples

1Ethnologue catalogs over 7,000 living languages.
2Despite only containing 11 languages, TYDI QA covers

a large variety of linguistic phenomena and data scenarios.

Transactions of the Association for Computational Linguistics, Bd. 8, S. 454–470, 2020. https://doi.org/10.1162/tacl a 00317
Action Editor: Eneko Agirre. Submission batch: 11/2019; Revision batch: 1/2020; Published 7/2020.
C(cid:13) 2020 Verein für Computerlinguistik. Distributed under a CC-BY 4.0 Lizenz.

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of some relevant phenomena drawn from the
data to provide researchers with a sense of the
challenges present in non-English text that their
models will need to handle (Abschnitt 5). We also
provide an open-source baseline model3 and a
public leaderboard4 with a hidden test set to track
community progress. We hope that enabling such
intrinsic and extrinsic analyses on a challenging
task will spark progress in multilingual modeling.
The underlying data of a research study can
have a strong influence on the conclusions that
will be drawn: Is QA solved? Do our models
accurately represent a large variety of languages?
Attempting to answer
these questions while
experimenting on artificially easy datasets may
result in overly optimistic conclusions that lead
the research community to abandon potentially
fruitful lines of work. We argue that TYDI QA will
enable the community to reliably draw conclusions
that are aligned with people’s information-seeking
needs while exercising systems’ ability to handle
a wide variety of language phenomena.

2 Task Definition

TYDI QA presents a model with a question along
with the content of a Wikipedia article, Und
requests that it make two predictions:

1. Passage Selection Task: Given a list of the
passages in the article, return either (A) Die
index of the passage that answers the question
oder (B) NULL if no such passage exists.

2. Minimal Answer Span Task: Given the full
text of an article, return one of (A) the start
and end byte indices of the minimal span
that completely answers the question; (B)
YES or NO if the question requires a yes/no
answer and we can draw a conclusion from
the passage; (C) NULL if it is not possible to
produce a minimal answer for this question.

Figur 1 shows an example question-answer
pair. This formulation reflects that information-
seeking users do not know where the answer

to their question will come from, nor is it always
obvious whether their question is even answerable.

Figur 1: An English example from TYDI QA. Der
answer passage must be selected from a list of passages
in a Wikipedia article while the minimal answer is
some span of bytes in the article (bold). Many questions
have no answer.

3 Data Collection Procedure

Question Elicitation: Human annotators are
given short prompts consisting of the first 100
characters of Wikipedia articles and asked to write
questions that (A) they are actually interested
in knowing the answer to, Und (B) that are not
answered by the prompt (see Section 3.1 für
the importance of unseen answers). The prompts
are provided merely as inspiration to generate
questions on a wide variety of topics; annotators
are encouraged to ask questions that are only
vaguely related to the prompt. Zum Beispiel, gegeben
the prompt Apple is a fruit. . . , an annotator
might write What disease did Steve Jobs die of?
We believe this stimulation of curiosity reflects
how questions arise naturally: People encounter
a stimulus such as a scene in a movie, a dog on
the street, or an exhibit in a museum and their
curiosity results in a question.

Our question elicitation process is similar to
QuAC in that question writers see only a small
snippet of Wikipedia content. Jedoch, QuAC
annotators were requested to ask about a particular
entity while TYDI QA annotators were encouraged
to ask about anything interesting that came to
Geist, no matter how unrelated. This allows the
question writers even more freedom to ask about
topics that truly interest them, including topics not
covered by the prompt article.

Article Retrieval: A Wikipedia article5 is then
paired with each question by performing a
Google search on the question text, eingeschränkt
to the Wikipedia domain for each language, Und
selecting the top-ranked result. To enable future
use cases, article text is drawn from an atomic
Wikipedia snapshot of each language.6

3github.com/google-research-datasets/

tydiqa.

4ai.google.com/research/tydiqa.

5We removed tables, long lists, and info boxes from the
articles to focus the modeling challenge on multilingual text.
6Each snapshot corresponds to an Internet Archive URL.

455

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to select

annotators

Answer Labeling: Endlich,
Sind
presented with the question/article pair and asked
the best passage answer—a
Erste
paragraph7
in the article that contains an
answer—or else indicate that no answer is possible
(or that no single passage is a satisfactory answer).
If such a passage is found, annotators are asked to
select, if possible, a minimal answer: A character
span that is as short as possible while still forming
a satisfactory answer to the question; ideally, diese
are 1–3 words long, but in some cases can span
most of a sentence (z.B., for definitions such as
What is an atom?). If the question is asking for a
Boolean answer, the annotator selects either YES
or NO. If no such minimal answer is possible, Dann
the annotators indicate this.

3.1 The Importance of Unseen Answers

Our question writers seek information on a
they find interesting yet somewhat
topic that
unfamiliar. When questions are formed without
knowledge of the answer,
the questions tend
to contain (A) underspecification of questions,
such as What is sugar made from?—Did the
asker intend a chemical formula or the plants
it is derived from?—and (B) mismatches of the
lexical choice and morphosyntax between the
question and answer since the question writers
are not cognitively primed to use the same words
and grammatical constructions as some unseen
Antwort. The resulting question-answer pairs avoid
many typical artifacts of QA data creation such
as high lexical overlap, which can be exploited
by machine learning systems to artificially inflate
task performance.8

We see this difference borne out in the leader-
boards of datasets in each category: datasets
where question writers saw the answer are mostly
solved—for example, SQuAD (Rajpurkar et al.,
2016, 2018) and CoQA (Reddy et al., 2019);
datasets whose question writers did not see
the answer text remain largely unsolved—for
Beispiel,
the Natural Questions (Kwiatkowski
et al., 2019) and QuAC. Ähnlich, Lee et al. (2019)
found that question answering datasets in which
questions were written while annotators saw the

7Or other roughly paragraph-like HTML element.
8Compare these information-seeking questions with
carefully crafted reading comprehension or trivia questions
that should have an unambiguous answer. Dort, expert
question askers have a different purpose: to validate the
knowledge of the potentially expert question answerer.

456

answer text tend to be easily defeated by TF-IDF
approaches that rely mostly on lexical overlap
whereas datasets where question-writers did not
know the answer benefited from more powerful
Modelle. Put another way, artificially easy datasets
may favor overly simplistic models.

Unseen answers provide a natural mechanism
for creating questions that are not answered by
the text since many retrieved articles indeed do
not contain an appropriate answer. In SQuAD 2.0
(Rajpurkar et al., 2018), unanswerable questions
were artificially constructed.

3.2 Why Not Translate?

introduce

One approach to creating multilingual data is to
translate an English corpus into other languages,
as in XNLI (Conneau et al., 2018). Jedoch,
translation—including human
the process of
translation—tends
problematic
artifacts to the output language such as preserving
source-language word order as when translating
from English to Czech (which allows flexible word
Befehl) or the use of more constrained language
by translators (z.B., more formal). The result is
that a corpus of so-called Translationese may
be markedly different from purely native text
(Lembersky et al., 2012; Volansky et al., 2013;
Avner et al., 2014; Eetemadi and Toutanova, 2014;
Rabinovich and Wintner, 2015; Wintner, 2016).
Questions that originate in a different language
may also differ in what is left underspecified or
in what topics will be discussed. Zum Beispiel,
in TYDI QA, one Bengali question asks What
does sapodilla taste like?, referring to a fruit
that is unlikely to be mentioned in an English
corpus, presenting unique challenges for transfer
learning. Each of these issues makes a translated
corpus more English-like, potentially inflating the
apparent gains of transfer-learning approaches.

Two recent multilingual QA datasets have used
this approach. MLQA (Lewis et al., 2019) includes
12k SQuAD-like English QA instances; a subset
of articles are matched to six target language
articles via a multilingual model and the associated
questions are translated. XQuAD (Artetxe et al.,
2019) includes 1,190 QA instances from SQuAD
1.1, with both questions and articles translated
into 10 languages.9 Compared with TYDI QA,
these datasets are vulnerable to Translationese

9XQuAD translators see English questions and passages

gleichzeitig, priming them to use similar words.

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while MLQA’s use of a model-in-the-middle to
match English answers to target language answers
comes with some risks: (1) of selecting answers
containing machine-translated Wikipedia content;
Und (2) of the dataset favoring models that are
trained on the same parallel data or that use a
similar multilingual model architecture.

3.3 Document-Level Reasoning

TYDI QA requires reasoning over lengthy articles
(5K–30KB avg., Tisch 4) and a substantial portion
of questions (46%–82%) cannot be answered
by their article. This is consistent with the
information-seeking scenario: the question asker
does not wish to specify a small passage to scan for
answers, nor is an answer guaranteed. In SQuAD-
style datasets such as MLQA and XQuAD, Die
model is provided only a paragraph that always
contains the answer. Full documents allow TYDI
QA to embrace the natural ambiguity over correct
answers, which is often correlated with difficult,
interesting questions.

3.4 Quality Control

To validate the quality of questions, we sampled
questions from each annotator and verified with
native speakers that the text was fluent.10 We also
verified that annotators were not asking ques-
tions answered by the prompts. We provided
minimal guidance about acceptable questions,
discouraging only categories such as opinions
(z.B., What is the best kind of gum?) and conver-
sational questions (z.B., Who is your favorite
football player?).

Answer labeling required more training, Par-
ticularly defining minimal answers. Zum Beispiel,
should minimal answers include function words?
Should minimal answers for definitions be full
Sätze? (Our guidelines specify no to both).
Annotators performed a training task, requiring
90%+ to qualify. This training task was repeated
throughout data collection to guard against
annotators drifting off the task definition. Wir
monitored inter-annotator agreement during data
Sammlung. For the dev and test sets,11 a sepa-
rate pool of annotators verified the questions

10Small typos are acceptable as they are representative of

how real users interact with QA.

11Except Finnish and Kiswahili.

and minimal answers to ensure that
acceptable.12

they are

4 Related Work

In addition to the various datasets discussed
throughout Section 3, multilingual QA data has
also been generated for very different
tasks.
Zum Beispiel, in XQA (Liu et al., 2019A) Und
XCMRC (Liu et al., 2019B), statements phrased
syntactically as questions (Did you know that
is the largest stringray?) are given as
prompts to retrieve a noun phrase from an article.
Kenter et al. (2018) locate a span in a document
that provides information on a certain property
such as location.

Prior to these, several non-English multilingual
question answering datasets have appeared,
typically including one or two languages: Diese
include DuReader (He et al., 2017) and DRCD
(Shao et al., 2018) in Chinese, French/Japanese
evaluation sets for SQuAD created via translation
(Asai et al., 2018), Korean translations of
SQuAD (Lee et al., 2018; Lim et al., 2019),
a semi-automatic Italian translation of SQuAD
(Croce et al., 2018), ARCD—an Arabic reading
comprehension dataset (Mozannar et al., 2019),
a Hindi-English parallel dataset in a SQuAD-like
setting (Gupta et al., 2018), and a Chinese–English
dataset focused on visual QA (Gao et al., 2015).
The recent MLQA and XQuAD datasets also
translate SQuAD in several
(sehen
Abschnitt 3.2). With the exception of DuReader,
these sets also come with the same lexical overlap
caveats as SQuAD.

languages

Outside of QA, XNLI

(Conneau et al.,
2018) has gained popularity for natural language
Verständnis. Jedoch, SNLI (Bowman et al.,
2015) and MNLI (Williams et al., 2018) can
be modeled surprisingly well while ignoring
the presumably critical premise (Poliak et al.,
2018). While NLI stress tests have been created
to mitigate these issues (Naik et al., 2018),
constructing a representative NLI dataset remains
an open area of research.

The question answering format encompasses a
wide variety of tasks (Gardner et al., 2019) reichend

12For questions, we accepted questions with minor typos
or dialect, but rejected questions that were obviously non-
einheimisch. For final-pass answer filtering, we rejected answers
that were obviously incorrect, but accept answers that are
plausible.

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from generating an answer word-by-word (Mitra,
2017) or finding an answer from within an entire
corpus as in TREC (Voorhees and Tice, 2000) Und
DrQA (Chen et al., 2017).

Question answering can also be interpreted
as an exercise in verifying the knowledge of
experts by finding the answer to trivia questions
that are carefully crafted by someone who
already knows the answer such that exactly
one answer is correct such as TriviaQA and
Quizbowl/Jeopoardy! Fragen (Ferrucci et al.,
2010; Dunn et al., 2017; Joshi et al., 2017; Peskov
et al., 2019); this information-verifying paradigm
also describes reading comprehension datasets
such as NewsQA (Trischler et al., 2017), SQuAD
(Rajpurkar et al., 2016, 2018), CoQA (Reddy
et al., 2019), and the multiple choice RACE (Lai
et al., 2017). This paradigm has been taken even
further by biasing the distribution of questions
toward especially hard-to-model examples as in
QAngaroo (Welbl et al., 2018), HotpotQA (Yang
et al., 2018), and DROP (Dua et al., 2019). Others
have focused exclusively on particular answer
types such as Boolean questions (Clark et al.,
2019). Recent work has also sought to bridge the
gap between dialog and QA, answering a series of
questions in a conversational manner as in CoQA
(Reddy et al., 2019) and QuAC (Choi et al., 2018).

5 Typological Diversity

Our primary criterion for including languages in
this dataset is typological diversity—that is, Die
degree to which they express meanings using
anders
linguistic devices, which we discuss
below. Mit anderen Worten, we seek to include not
just many languages, but many language families.
Außerdem, we select languages that have
diverse data characteristics that are relevant
to modeling. Zum Beispiel, some languages may
have very little monolingual data. There are many
languages with very little parallel translation data
and for which there is little economic incentive
to produce a large amount of expensive parallel
data in the near future. Approaches that rely
too heavily on the availability of high-quality
machine translation will fail to generalize across
the world’s languages. Aus diesem Grund, we select
some languages that have parallel training data
(z.B., Japanese, Arabic) and some that have
very little parallel training data (z.B., Bengali,

Kiswahili). Despite the much greater difficulties
involved in collecting data in these languages, Wir
expect that their diversity will allow researchers
to make more reliable conclusions about how well
their models will generalize across languages.

5.1 Discussion of Languages

We offer a comparative overview of linguistic
features of the languages in TYDI QA in Table 1.
To provide a glimpse into the linguistic phenom-
ena that have been documented in the TYDI QA
Daten, we discuss some of the most interesting
features of each language below. These are by no
means exhaustive, but rather intended to highlight
the breadth of phenomena that
this group of
languages covers.

Arabic: Arabic is a Semitic language with short
vowels indicated as typically-omitted diacritics.
Arabic employs a root-pattern system: a sequence
of consonants represents the root; letters vary
inside the root to vary the meaning. Arabic relies
on substantial affixation for inflectional and deri-
vational word formation. Affixes also vary by
grammatical number: singular, dual (zwei), Und
plural (Ryding, 2005). Clitics13 are common
(Attia, 2007).

Bengali: Bengali is a morphologically-rich lan-
Spur. Words may be complex due to inflection,
affixation, compounding, reduplication, and the id-
iosyncrasies of the writing system including non-
decomposable consonant conjuncts. (Thompson,
2010).

Finnish: Finnish is a Finno-Ugric language with
rich inflectional and derivational suffixes. Word
stems often alter due to morphophonological
alternations (Karlsson, 2013). A typical Finnish
noun has approximately 140 forms and a verb
um 260 Formen (Hakulinen et al., 2004).14

Japanese:
Japanese is a mostly non-configu-
rational15 language in which particles are used to
indicate grammatical roles though the verb typi-
cally occurs in the last position (Kaiser et al.,
2013). Japanese uses 4 alphabets: kanji (ideograms
shared with Chinese), hiragana (a phonetic alphabet

13Clitics are affix-like linguistic elements that may carry

grammatical or discourse-level meaning.

14Not counting forms derived through compounding or the

addition of particle clitics.

15Among other linguistics features, ‘non-configurational’

languages exhibit generally free word order.

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LANGUAGE

LATIN SCRIPT

a WHITE SPACE

ENGLISH
ARABIC
BENGALI
FINNISH
INDONESIAN
JAPANESE
KISWAHILI
KOREAN
RUSSIAN
TELUGU
THAI

+
—
—
+
+
—
+
—
—
—
—

TOKENS
+
+
+
+
+
—
+
+F
+
+
—

SENTENCE
BOUNDARIES
+
+
+
+
+
+
+
+
+
+
—

WORD
B
FORMATION

GENDER

PRO-DROP

+
++
+
+ + +
+
+
+ + +
+ + +
++
+ + +
+

+D
+
—
—
—
—
—e
—
+
+
+

—
+
+
—
+
+
+
+
+
+
+

a ‘—’ indicates Latin script is not the conventional writing system. Intermixing of Latin script should still be expected.
b We include inflectional and derivation phenomena in our notion of word formation.
c We limit the gender feature to sex-based gender systems associated with coreferential gendered personal pronouns.
d English has grammatical gender only in third person personal and possessive pronouns.
e Kiswahili has morphological noun classes (Corbett, 1991), but here we note sex-based gender systems.
f In Korean, tokens are often separated by white space, but prescriptive spacing conventions are commonly flouted.

Tisch 1: Typological features of the 11 languages in TYDI QA. Wir gebrauchen + to indicate that this phenomena occurs,
++ to indicate that it occurs frequently, Und + + + to indicate very frequently.

for morphology and spelling), katakana (A
phonetic alphabet for foreign words), und das
Latin alphabet (for many new Western terms); alle
of these are in common usage and can be found in
TYDI QA.

Indonesian:
Indonesian is an Austronesian
Sprache
characterized by reduplication of
nouns, pronouns, Adjektive, verbs, and numbers
(Sneddon et al., 2012; Vania and Lopez, 2017), als
well as prefixes, suffixes, infixes, and circumfixes.

Kiswahili: Kiswahili
is a Bantu language
with complex inflectional morphology. Unlike
the majority of world languages,
inflections,
like number and person, are encoded in the
the suffix (Ashton, 1947). Noun
prefix, nicht
modifiers show extensive agreement with the
noun class (Mohamed, 2001). Kiswahili is a pro-
drop language16 (Seidl and Dimitriadis, 1997;
Wald, 1987). Most semantic relations that would
be represented in English as prepositions are
expressed in verbal morphology or by nouns
(Wald, 1987).

Koreanisch: Korean is an agglutinative, predicate-
final language with a rich set of nominal and verbal
suffixes and postpositions. Nominal particles

16Both the subject and the object can be dropped due to

verbal inflection.

express up to 15 cases—including the connective
‘‘and’’/‘‘or’’—and can be stacked in order of
dominance from right to left. Verbal particles
express a wide range of
tense-aspect-mood,
and include a devoted ‘‘sentence-ender’’ for
declarative, interrogative, imperative, usw. Koreanisch
also includes a rich system of honorifics. There is
extensive discourse-level pro-drop (Sohn, 2001).
The written system is a non-Latin featural alphabet
arranged in syllabic blocks. White space is used in
writing, but prescriptive conventions for spacing
predicate-auxiliary compounds and semantically
close noun-verb phrases are commonly flouted
(Han and Ryu, 2005).

Russian: Russian is an Eastern Slavic language
using the Cyrillic alphabet. An inflected lan-
Spur, it relies on case marking and agreement to
represent grammatical
Verwendet
singular, paucal,17 and plural number. Substantial
fusional18 morphology (Comrie, 1989) is used
along with three grammatical genders (Corbett,
1982), extensive pro-drop (Bizzarri, 2015), Und
flexible word order (Bivon, 1971).

roles. Russian

17Paucal number represents a few instances—between
singular and plural. In Russian, paucal is used for quantities
von 2, 3, 4, and many numerals ending in these digits.

18Fusional morphology expresses several grammatical

categories in one unsegmentable element.

459

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Figur 2: Finnish example exhibiting compounding,
inflection, and consonant gradation. In the question,
weekdays is a compound. Jedoch, in the compound,
week is inflected in the genitive case -n and the change
of kk to k in the stem (a common morphophonological
process in Finnish known as consonant gradation). Der
plural is marked on the head of the compound day by
the plural suffix -t. But in the answer, Week is present
as a standalone word in the nominative case (no overt
case marking), but is modified by a compound adjective
composed of seven and days.

inconsistent name
Figur 4: Arabic example of
spellings; both spellings are correct and refer to the
same entity.

Figur 5: Arabic example of selective diacritization.
Note that the question contains diacritics (short vowels)
to emphasize the pronunciation of AlEumAny (Die
specific entity intended) while the answer does not
have diacritics in EmAn.

Figur 6: Arabic example of name de-spacing. Der
name appears as AbdulSalam in the question and
Abdul Salam in the answer. This is potentially because
of the visual break in the script between the two parts
of the name. In manual orthography, the presence of
the space would be nearly undetectable; its existence
becomes an issue only in the digital realm.

Figur 3: Russian example of morphological variation
across question-answer pairs due to the difference in
syntactic context: the entities are identical but have
different representation, making simple string matching
more difficult. The names of the planets are in the
Thema (Uran, Uranus-NOM) and object of the prep-
osition (ot zemli, from Earth-GEN) context in the
question. The relevant passage with the answer has the
names of the planets in a coordinating phrase that is an
object of a preposition (me(cid:25)du Uranom i Zeml(cid:27)(cid:26)
between Uranus-INSTR and Earth-INSTR). Because the
syntactic contexts are different, the names of the planets
have different case marking.

Figur 7: Arabic example of gender variation of the
word first (Awl vs Al>wlY) between the question and
Antwort.

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Telugu: Telugu is
a Dravidian language.
Orthographically, consonants are fully specified
and vowels are expressed as diacritics if they
differ from the default syllable vowel. Telugu
is an agglutinating, suffixing language (Lisker,
1963; Krishnamurti, 2003). Nouns have 7–8
Fälle, singular/plural number, and three genders
(feminine, masculine, neuter). An outstanding
feature of Telugu is a productive process
für
forming transitives and causative forms
(Krishnamurti, 1998).

Thai: Thai is an analytic language19 despite very
infrequent use of white space: Spacing in Thai is
usually used to indicate the end of a sentence but
may also indicate a phrase or clause break or
appear before or after a number (D¯anwiwat, 1987).

5.2 A Linguistic Analysis

While the field of computational linguistics has
remained informed by its roots in linguistics,
practitioners often express a disconnect: Descrip-
tive linguists focus on fascinating complex phe-
gegeben, yet datasets that computational linguists
encounter often do not contain such examples.
TYDI QA is intended to help bridge this gap:
we have identified and annotated examples from
the data that exhibit linguistic phenomena that
(A) are typically not found in English and (B) Sind
potentially problematic for NLP models.

Figur 2 presents the interaction among three
phenomena in a Finnish example, and Figure 3
shows an example of non-trivial word form
changes due to inflection in Russian. Arabic also
exemplifies many phenomena that are likely to
challenge current models including spelling varia-
tion of names (Figur 4), selective diacritization of
Wörter (Figur 5), inconsistent use of whitespace
(Figur 6), and gender variation (Figur 7).

These examples illustrate that

the subtasks
that are nearly trivial in English—such as string
matching—can become complex for languages
where morphophonological alternations and com-
pounding cause dramatic variations in word forms.

6 A Quantitative Analysis

At a glance, TYDI QA consists of 204K examples:
to be used for
166K are one-way annotated,

19An analytic language uses helper words rather than
morphology to express grammatical relationships. Addi-
tional glossed examples are available at ai.google.com/
research/tydiqa.

QUESTION WORD TYDI QA SQuAD

WHAT
HOW
WHEN
WHERE
(YES/NO)
WHO
WHICH
WHY

30%
19%
14%
14%
10%
9%
3%
1%

51%
12%
8%
5%
<1% 11% 5% 2% Table 2: Distribution of question words in the English portion of the development data. NULL PASSAGE ANSWER MINIMAL ANSWER 85% 92% 93% Table 3: Expert judgments of annotation accuracy. NULL indicates how often the annotation is correct given that an annotator marked a NULL answer. Passage answer and minimal answer indicate how often each is correct given the annotator marked an answer. training, and 37K are 3-way annotated, comprising the dev and test sets, for a total of 277K annotations (Table 4). 6.1 Question Analysis While we strongly suspect that the relationship between the question and answer is one of the best indicators of a QA dataset’s difficulty, we also provide a comparison between the English question types found in TYDI QA and SQuAD in Table 2. Notably, TYDI QA displays a more balanced distribution of question words.20 6.2 Question-Prompt Analysis We also evaluate how effectively the annotators followed the question elicitation protocol of Section 3. From a sample of 100 prompt–question pairs, we observed that all questions had 1–2 words of overlap with the prompt (typically an entity or word of interest) and none of the questions were answered by the prompt, as requested. Because these prompts are entirely discarded in the final 20For non-English languages, it is difficult to provide an intuitive analysis of question words across languages since question words can function differently depending on context. 461 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 3 1 7 1 9 2 3 3 4 8 / / t l a c _ a _ 0 0 3 1 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Language Train (1-way) Dev (3-way) Test (3-way) (English) Arabic Bengali Finnish Indonesian Japanese Kiswahili Korean Russian Telugu Thai TOTAL 9,211 23,092 10,768 15,285 14,952 16,288 17,613 10,981 12,803 24,558 11,365 1031 1380 328 2082 1805 1709 2288 1698 1625 2479 2245 166,916 18,670 1046 1421 334 2065 1809 1706 2278 1722 1637 2530 2203 18,751 Avg. Avg. Question Article Answer Avg. Tokens 7.1 5.8 7.5 4.9 5.6 — 6.8 5.1 6.5 5.2 — Bytes 30K 14K 13K 19K 11K 14K 5K 12K 27K 7K 14K Bytes 57 114 210 74 91 53 39 67 106 279 171 Avg. Passage % With %With Passage Minimal Candidates Answer Answer 42% 69% 35% 41% 34% 32% 22% 22% 51% 27% 43% 50% 76% 38% 49% 38% 41% 24% 26% 64% 28% 54% 47 34 34 35 32 52 35 67 74 32 38 Table 4: Data statistics. Data properties vary depending on languages, as documents on Wikipedia differ significantly and annotators don’t overlap between languages. We include a small amount of English data for debugging purposes, though we do not include English in macro-averaged results, nor in the leaderboard competition. Note that a single character may occupy several bytes in non-Latin alphabets. the questions often have less lexical dataset, overlap with their answers than the prompts. 6.3 Data Quality In Table 3, we analyze the degree to which the annotations are correct.21 Human experts22 carefully judged a sample of 200 question–answer pairs from the dev set for Finnish and Russian. For each question, the expert indicates (1) whether or not each question has an answer within the article—the NULL column, (2) whether or not each of the three passage answer annotations is correct, and (3) whether the minimal answer is correct. We take these high accuracies as evidence that the quality of the dataset provides a useful and reliable signal for the assessment of multilingual question answering models. Looking into these error patterns, we see that the NULL-related errors are entirely false positives (failing to find answers that exist), which would largely be mitigated by having three answer annotations. Such errors occur in a variety of article lengths from under 1,000 words through large 3,000-word articles. Therefore, we cannot 21We measure correctness instead of inter-annotator agreement since question may have multiple correct answers. For example, We have observed a yes/no question where both YES and NO were deemed correct. Aroyo (2015) discuss the pitfalls of over-constrained annotation guidelines in depth. 22Trained linguists with experience in NLP data collection. attribute NULL errors to long articles alone, but we should consider alternative causes such as some question–answer matching being more difficult or subtle. For minimal answers, errors occur for a large variety of reasons. One error category is when multiple dates seem plausible but only one is correct. One Russian question reads When did Valentino Rossi win the first title?. Two annotators correctly selected 1997 while one selected 2001, which was visually prominent in a large list of years. 7 Evaluation 7.1 Evaluation Measures We now turn from analyzing the quality of the data itself toward how to evaluate question answering systems using the data. The TYDI QA task’s primary evaluation measure is F1, a harmonic mean of precision and recall, each of which is calculated over the examples within a language. However, certain nuances do arise for our task. NULL Handling: TYDI QA is an imbalanced dataset in terms of whether or not each question has an answer due to differing amounts of content in each language on Wikipedia. However, it is undesirable if a strategy such as always predicting NULL can produce artificially inflated results—this 462 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 3 1 7 1 9 2 3 3 4 8 / / t l a c _ a _ 0 0 3 1 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Train Size 9,211 23,092 10,768 15,285 14,952 16,288 17,613 10,981 12,803 24,558 11,365 166,916 (English) Arabic Bengali Finnish Indonesian Japanese Kiswahili Korean Russian Telugu Thai OVERALL First passage 32.9(28.4/39.1) 64.7(59.2/71.3) 21.4(15.5/34.6) 35.4(28.4/47.1) 32.6(23.8/51.7) 19.4(14.8/28.0) 20.3(13.4/42.0) 19.9(13.1/41.5) 30.0(25.5/36.4) 23.3(15.1/50.9) 34.7(27.8/46.4) 30.2(23.6/45.0) Passage Answer F1 (P/R) mBERT 62.5(62.6/62.5) 81.7(85.7/78.1) 60.3(61.4/59.5) 60.8(58.7/63.0) 61.4(57.2/66.7) 40.6(42.2/39.5) 60.2(58.4/62.3) 56.8(58.7/55.3) 63.2(65.3/61.2) 81.3(81.7/80.9) 64.7(61.8/68.0) 63.1(57.0/59.1) Lesser Human 69.4(63.4/77.6) 85.4(82.1/89.0) 85.5(81.6/89.7) 76.3(69.8/84.2) 78.6(72.7/85.6) 65.1(57.8/74.8) 76.8(70.1/85.0) 72.9(66.3/82.4) 87.2(84.4/90.2) 95.0(93.3/96.8) 76.1(69.9/84.3) 79.9(84.4/74.5) Minimal Answer Span F1 (P/R) mBERT 44.0(52.9/37.8) 69.3(74.9/64.5) 47.7(50.7/45.3) 48.0(56.7/41.8) 51.3(54.5/48.8) 30.4(42.1/23.9) 49.7(55.2/45.4) 40.1(45.2/36.2) 45.8(51.7/41.2) 74.3(77.7/71.3) 48.3(54.3/43.7) 50.5(41.3/35.3) Lesser Human 54.4(52.9/56.5) 73.5(73.6/73.5) 79.1(78.6/79.7) 65.3(61.8/69.4) 71.1(68.7/73.7) 53.3(51.8/55.2) 67.4(63.4/72.1) 56.7(56.3/58.6) 76.0(82.0/70.8) 93.3(91.6/95.2) 65.6(63.9/67.9) 70.1(70.8/62.4) Table 5: Quality on the TYDI QA primary tasks (passage answer and minimal answer) using: a na¨ıve first-passage baseline, the open-source multilingual BERT model (mBERT), and a human predictor (Section 7.3). F1, precision, and recall measurements (Section 7.1) are averaged over four fine-tuning replicas for mBERT. would indeed be the case if we were to give credit to a system producing NULL if any of the three annotators selected a NULL answer. Therefore, we first use a threshold to select a NULL consensus for each evaluation example: At least two of the three annotators must select an answer for the consensus to be non-NULL. The NULL consensus for the given task (passage answer, minimal answer) must be NULL in order for a system to receive credit (see below) for a NULL prediction. Passage Selection Task: For questions having a NULL consensus (see above), credit is given for matching any of the passage indices selected by annotators.23 An example counts toward the denominator of it has a non- recall NULL consensus, and toward the denominator of precision if the model predicted a non-NULL answer. if Minimal Span Task: For each example, given the question and text of an article, a system must predict NULL, YES, NO, or a contiguous span of bytes that constitutes the answer. For span answers, we treat this collection of byte index pairs as a set and compute an example-wise F1 score between each annotator’s minimal answer and the model’s minimal answer, with partial credit assigned when spans are partially overlapping; the maximum is returned as the score for each example. For a YES/NO answers, credit is given 23By matching any passage, we effectively take the max over examples, consistent with the minimal span task. (a score of 1.0), if any of the annotators indicated such as a correct answer. The NULL consensus must be non-NULL in order to receive credit for a non-NULL answer. Macro-Averaging: First, the scores for each example are averaged within a language; we then average over all non-English languages to obtain a final F1 score. Measurements on English are treated as a useful means of debugging rather than a goal of the TYDI QA task as there is already plenty of coverage for English evaluation in existing datasets. 7.2 An Estimate of Human Performance In this section, we consider two idealized methods for estimating human performance before settling on a widely used pragmatic method. A Fair Contest: As a thought experiment, con- sider framing evaluation as ‘‘What is the likeli- hood that a correct answer is accepted as correct?’’ Trivia competitions and game shows take this approach as they are verifying the expertise of human answers. One could exhaustively enumerate all correct passage answers; given several annotations of high accuracy, we would quickly obtain high recall. This approach is advocated in Boyd-Graber (2019). A Game with Preferred Answers: If our goal is to provide users with the answers that they prefer. If annotators correctly choose these preferred answers, we expect our multi-way annotated data to contain a distribution peaked around 463 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 3 1 7 1 9 2 3 3 4 8 / / t l a c _ a _ 0 0 3 1 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 these preferred answers. The optimal strategy for players is then to predict those answers, which are both preferred by users and more likely to be in the evaluation dataset. We would expect a large pool of human annotators or a well-optimized machine learning system to learn this distribution. For example, the Natural Questions (Kwiatkowski et al., 2019) uses a 25-way annotations to construct increasing the estimate of a super-annotator, human performance by around 15 points F1. A Lesser Estimate of Human Performance: Unfortunately, finding a very large pool of annotators for 11 languages would be prohibitively expensive. Instead, we provide a more pessimistic estimate of human performance by holding out one human annotation as a prediction and evaluating it against the other two annotations; we use bootstrap resampling to repeat this procedure for all possible combinations of 1 vs. 2 annotators. This corresponds to the human evaluation methodology for SQuAD with the addition of bootstrapping to reduce variance. In Table 5, we show this estimate of human performance. In cases where annotators disagree, this estimate will degrade, which may lead to an underestimate of human performance since in reality multiple answers could be correct. At first glance, these F1 scores may appear low compared to simpler tasks such as SQuAD, yet a single human prediction on the Natural Questions short answer task (similar to the TYDI QA minimal answer task), scores only 57 F1 even with the advantage of evaluating against five annotations rather than just two and training on 30X more English training data. 7.3 Primary Tasks: Baseline Results To provide an estimate of the difficulty of this dataset for well-studied state-of-the-art models, we present results for a baseline that uses the most recently released multilingual BERT (mBERT)24 (Devlin et al., 2019) in a setup similar to Alberti et al. (2019), in which all languages are trained jointly in a single model (Table 5). Additionally, as a na¨ıve, untrained baseline, we include the results of a system that always predicts the first passage, since the first paragraph of a Wikipedia article often summarizes its most important facts. Across all languages, we see a large gap between mBERT and a lesser estimate of human performance (Section 7.2). TYDIQA-GOLDP MLQA XQuAD (English) Arabic Bengali Finnish Indonesian Kiswahili Korean Russian Telugu 0.38 0.26 0.29 0.23 0.41 0.31 0.19 0.16 0.13 0.91 0.61 — — — — — — — 1.52 1.29 — — — — — 1.13 — Table 6: Lexical overlap statistics for TYDIQA- GOLDP, MLQA, and XQuAD showing the average number of tokens in common between the question and a 200-character window around the answer span. As expected, we observe substantially lower lexical overlap in TYDI QA. Can We Compare Scores Across Languages? its Unfortunately, no. Each language has own unique set of questions, varying quality and amount of Wikipedia content, quality of annotators, and other variables. We believe it to directly engage with these issues; is best avoiding these phenomena may hide important aspects of the problem space associated with these languages. 8 Gold Passage: A Simplified Task Up to this point, we have discussed the primary tasks of Passage Selection (SELECTP) and Minimal Answer Span (MINSPAN). In this section, we describe a simplified Gold Passage (GOLDP) task, which is more similar to existing reading comprehension datasets, with two goals in mind: (1) more directly comparing with prior work, and (2) providing a simplified way for researchers to use TYDI QA by providing compatibility with existing code for SQuAD, XQuAD, and MLQA. Toward these goals, the Gold Passage task differs from the primary tasks in several ways: • only the gold answer passage is provided rather than the entire Wikipedia article; • unanswerable questions have been discarded, similar to MLQA and XQuAD; • we evaluate with the SQuAD 1.1 metrics like 24github.com/google-research/bert. XQuAD; and 464 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 3 1 7 1 9 2 3 3 4 8 / / t l a c _ a _ 0 0 3 1 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 TYDIQA- GOLDP SQuAD Zero Shot Human 8.2 Gold Passage Results (English) (76.8) (73.4) (84.2) Arabic Bengali Finnish Indonesian Kiswahili Korean Russian Telugu OVERALL 81.7 75.4 79.4 84.8 81.9 69.2 76.2 83.3 79.0 60.3 57.3 56.2 60.8 52.9 50.0 64.4 49.3 56.4 85.8 94.8 87.0 92.0 92.0 82.0 96.3 97.1 90.9 Table 7: F1 scores for the simplified TYDIQA- GOLDP task v1.1. Left: Fine tuned and evaluated on the TYDIQA-GOLDP set. Middle: Fine tuned on SQuAD v1.1 and evaluated on the TYDIQA-GOLDP dev set, following the XQuAD zero-shot setting. Right: Estimate of human performance on TYDIQA-GOLDP. Models are averaged over five fine tunings. • Thai and Japanese are removed because the lack of white space breaks some existing tools. To better estimate human performance, only passages having 2+ annotations are retained. Of these annotations, one is withheld as a human prediction and the remainder are used as the gold set. 8.1 Gold Passage Lexical Overlap In Section 3, we argued that unseen answers and no translation should lead to a more complex, subtle relationship between the resulting questions and answers. We measure this directly in Table 6, showing the average number of tokens in common between the question and a 200-character window around the answer span, excluding the top 100 most frequent tokens, which tend to be non- content words. For all languages, we see a substantially lower lexical overlap in TYDI QA as compared to MLQA and XQuAD, corpora whose generation procedures involve seen answers and translation; we also see overall lower lexical overlap in non-English languages. We take this as evidence of a more complex relationship between questions and answers in TYDI QA. 465 In Table 7, we show the results of two experiments on this secondary Gold Passage task. First, we fine tune mBERT jointly on all languages of the TYDI QA gold passage training data and evaluate on its dev set. Despite lacking several of the core challenges of TYDI QA (e.g., no long articles, no unanswerable questions), F1 scores remain low, leaving headroom for future improvement. Second, we fine tune on the 100k English-only SQuAD 1.1 training set and evaluate on the full TYDI QA gold passage dev set, following the XQuAD evaluation zero-shot setting. We again observe very low F1 scores. These are similar to, though somewhat lower than, the F1 scores observed in the XQuAD zero-shot setting of Artetxe et al. (2019). Strikingly, even the English performance is significantly lower, demonstrating that the style of question–answer pairs in SQuAD have very limited value in training a model for TYDI QA-style questions, despite the much larger volume of English questions in SQuAD. 9 Recommendations and Future Work We foresee several research directions where this data will allow the research community to push new boundaries, including: • studying the interaction between morphology and question–answer matching; • evaluating the effectiveness of transfer learning, both for languages where parallel data is and is not available; • the usefulness of machine translation in question answering for data augmentation and as a runtime component, given varying data scenarios and linguistic challenges;25 and • studying zero-shot QA by explicitly not the provided training on a subset of languages. We also believe that a deeper understanding of the data itself will be key and we encourage further linguistic analyses of the data. Such insights will help us understand what modeling techniques will 25Because we believe that MT may be a fruitful research direction for TYDI QA, we do not release any automatic translations. In the past, this seems to have stymied innovation around translation as applied to multilingual datasets. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 3 1 7 1 9 2 3 3 4 8 / / t l a c _ a _ 0 0 3 1 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 be better-suited to tackling the full variety of phenomena observed in the world’s languages. Finally, we thank the anonymous reviewers for their helpful feedback. We recognize that no single effort will be sufficient to cover the world’s languages, and so we invite others to create compatible datasets for other languages; the universal dependency treebank (Nivre et al., 2016) now has over 70 languages, demonstrating what the community is capable of with broad effort.26 Finally, we note that the content required to answer questions often has simply not been written down in many languages. For these languages, we are paradoxically faced with the prospect that cross-language answer retrieval and translation are necessary, yet low-resource languages will also lack (and will likely continue to lack) the parallel data needed for trustworthy translation systems. 10 Conclusion Confidently making progress on multilingual models requires challenging, trustworthy evalu- ations. We have argued that question answering is well suited for this purpose and that by targeting a typologically diverse set of languages, progress on the TYDI QA dataset is more likely to general- ize on the breadth of linguistic phenomena found throughout the world’s languages. By avoiding data collection procedures reliant on translation and multilingual modeling, we greatly mitigate the risk of sampling bias. We look forward to the many ways the research community finds to improve the quality of multilingual models. Acknowledgments The authors wish to thank Chris Dyer, Daphne Luong, Dipanjan Das, Emily Pitler, Jacob Devlin, Jason Baldridge, Jordan Boyd-Graber, Kenton Lee, Kristina Toutanova, Mohammed Attia, Slav Petrov, and Waleed Ammar for their support, help analyzing data, and many insightful discussions about this work. We also thank Fadi Biadsy, Geeta Madhavi Kala, Iftekhar Naim, Maftuhah Ismail, Rola Najem, Taku Kudo, and Takaki Makino for their help in proofing the data for quality. We acknowledge Ashwin Kakarla and Karen Yee for support in data collection for this project. 26We will happily share our annotation protocol on request. 466 References Chris Alberti, Kenton Lee, and Michael Collins. the natural 2019. A BERT baseline for questions. arXiv preprint arXiv:1901.08634. Lora Aroyo and Chris Welty. 2015. Truth is a lie: Crowd truth and the seven myths of human annotation. AI Magazine, 36:15–25. Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. cross-lingual transferability of monolingual representations. arXiv preprint arXiv:1910.11856. 2019. On the Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2018. Multilin- gual extractive reading comprehension by runtime machine translation. arXiv preprint arXiv:1809.03275. Ethel O. Ashton. 1947. Swahili Grammar. 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