FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation

Parker Riley∗, Timothy Dozat∗, Jan A. Botha∗, Xavier Garcia∗,
Dan Garrette, Jason Riesa, Orhan Firat, Noah Constant
Google Research, Etats-Unis
{prkriley,tdozat,jabot,xgarcia,dhgarrette,
riesa,orhanf,nconstant}@google.com

Abstrait

We present FRMT, a new dataset and evalu-
ation benchmark for Few-shot Region-aware
Machine Translation, a type of style-targeted
translation. The dataset consists of professional
translations from English into two regional
variants each of Portuguese and Mandarin
Chinese. Source documents are selected to
enable detailed analysis of phenomena of in-
terest, including lexically distinct terms and
distractor terms. We explore automatic evalua-
tion metrics for FRMT and validate their cor-
relation with expert human evaluation across
both region-matched and mismatched rating
scenarios. Enfin, we present a number of
baseline models for this task, and offer guide-
lines for how researchers can train, evaluate,
and compare their own models. Our dataset
and evaluation code are publicly available:
https://bit.ly/frmt-task.

Introduction

Machine translation (MT) has made rapid ad-
vances in recent years, achieving impressive
performance for many language pairs, especially
those with high amounts of parallel data available.
Although the MT task is typically specified at the
coarse level of a language (par exemple., Spanish or Hindi),
some prior work has explored finer-grained dis-
tinctions, such as between regional varieties of
Arabic (Zbib et al., 2012), or specific levels of
politeness in German (Sennrich et al., 2016).
Malheureusement, most approaches to style-targeted
translation thus far rely on large, labeled train-
ing corpora (Zbib et al., 2012; Lakew et al., 2018;
Costa-juss`a et al., 2018; Honnet et al., 2018; Sajjad
et coll., 2020; Wan et al., 2020; Kumar et al., 2021),
and in many cases these resources are unavail-
able or expensive to create.

∗Equal contribution.

671

We explore a setting for MT where unla-
beled training data is plentiful for the desired
language pair, but only a few parallel examples
(0–100, called ‘‘exemplars’’) are annotated for
the target varieties. As a specific use-case, we ex-
amine translation into regional varieties: Brazilian
vs. European Portuguese and Mainland vs. Tai-
wan Mandarin. While these varieties are mutually
intelligible, they often exhibit lexical, syntactic, ou
orthographic differences that can negatively im-
pact an MT user’s experience. Chiffre 1 illustrates
the use of exemplars to control the regional variety
at inference time.

MT systems that do not support region or style
distinctions may be biased toward varieties with
more available data (the ‘‘web-majority’’ vari-
eties). We observe this bias in a widely used
proprietary MT system, with measurable nega-
tive effects for speakers of web-minority varieties
(§6.2). One barrier to further research on this is-
sue is the lack of a high-quality evaluation bench-
mark. Ainsi, to encourage more access to language
technologies for speakers of web-minority varie-
ties and more equitable NLP research, we make
the following contributions: (1) We construct
and release FRMT, a new dataset for evaluating
few-shot region-aware translation from English
to Brazilian/European Portuguese and Mainland/
Taiwan Mandarin. (2) We evaluate predictions
from a number of existing and custom-trained
baseline systems on the FRMT task using auto-
matic metrics. (3) We conduct detailed human
evaluations of gold and model-based translations
on FRMT, under all combinations of rater region
and target region. (4) We analyze the correlation
of automatic metrics and human evaluations on
FRMT, and propose a new targeted metric for
lexical accuracy.

2 Related Work

Textual style transfer aims to control fine-grained
stylistic features of generated text. Earlier work

Transactions of the Association for Computational Linguistics, vol. 11, pp. 671–685, 2023. https://doi.org/10.1162/tacl a 00568
Action Editor: Deyi Xiong. Submission batch: 11/2022; Revision batch: 1/2023; Published 6/2023.
c(cid:3) 2023 Association for Computational Linguistics. Distributed under a CC-BY 4.0 Licence.

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languages means that approaches performing well
on the entire FRMT benchmark can be expected
to generalize reasonably well to other languages,
other regions, and other stylistic attributes.

Several existing parallel corpora cover re-
gional language varieties, but have limitations that
motivate us to construct a new high-quality, tar-
geted dataset. e-PACT (Barreiro and Mota, 2017)
comprises translations from English books into
Portuguese variants, but is small and not easily
accessible. OpenSubTitles (Lison et al., 2018)
skews toward shorter utterances and is noisy due
to automatic alignment. WIT3 (Cettolo et al.,
2012) provides translations of TED-talk transcripts
into many languages, but relies on volunteer trans-
lators, which may limit quality.

Popular shared tasks have not included region-
targeted translation either: The Conference on
Machine Translation (WMT) has included trans-
lation between similar languages (par exemple., Akhbardeh
et coll., 2021), while the Workshop on NLP for Sim-
ilar Languages, Varieties and Dialects (VarDial)
focuses mainly on classification and not transla-
tion (par exemple., Zampieri et al., 2021).

En outre, we are not aware of previous
work that (1) measures deltas in human evaluation
metrics between the region-matched and region-
mismatched settings, (2) correlates these with
automated metrics, (3) offers tailored sub-tasks
targeting region-differentiated lexical items and
region-biased distractors, ou (4) defines targeted
metrics testing region-appropriateness.

3 FRMT Dataset

We introduce the FRMT dataset for evaluating
the quality of few-shot region-aware machine
translation. The dataset covers two regions each
for Portuguese (Brazil and Portugal) and Man-
darin (Mainland and Taiwan). These languages
and varieties were selected for multiple reasons:
(1) They have many speakers who can benefit
from increased regional support in NLP. (2) Por-
tuguese and Mandarin are linguistically very dis-
tinct, coming from different families; we therefore
hypothesize that methods that perform well on
both are more likely to generalize well to other
languages. The dataset was created by sampling
English sentences from Wikipedia and acquiring
professional human translations in the target re-
gional varieties. Final quality verification is done
through manual evaluation by an independent set

Chiffre 1: FRMT requires a machine translation model
to adapt its output to be appropriate for a specific re-
gion, such as Brazil (gauche) or Portugal (droite). Because
only a few exemplars are provided to convey the tar-
get region, methods that perform well on FRMT can
likely extend to other regions and styles.

leverages supervised parallel data (Jhamtani et al.,
2017); later work assumes labeled but non-parallel
training data (Shen et al., 2017; Li et al., 2018;
Niu et al., 2018un), or foregoes training-time labels
entirely, as in our setting, relying only on few-shot
exemplars provided at inference time (Xu et al.,
2020; Riley et al., 2021; Garcia et al., 2021).
Cependant, style transfer evaluation protocols are
known to be lacking (Pang and Gimpel, 2019;
Briakou et al., 2021; Hu et al., 2022), due to
the underspecification of stylistic attributes (par exemple.,
formality, sentiment) and the absence of standard-
ization across studies. Region-aware translation
addresses these issues, providing a test-bed for
exploring few-shot attribute control—MT evalu-
ation methods are relatively mature, and many
regional language varieties can be sufficiently
delineated for the task.

Previous work has explored many sub-types
of variety-targeted MT. Region-aware MT tar-
gets specific regions or dialects (Zbib et al., 2012;
Costa-juss`a et al., 2018; Honnet et al., 2018; Lakew
et coll., 2018; Sajjad et al., 2020; Wan et al., 2020;
Kumar et al., 2021; formality-aware MT targets
different formality levels (Niu et al., 2017, 2018b;
Wang et al., 2019); and personalized MT aims to
match an individual’s specific style (Michel and
Neubig, 2018; Vincent, 2021). Cependant, with few
exceptions (par exemple., Garcia et al., 2021), these works
assume the availability of large-scale datasets
containing examples with the target varieties ex-
plicitly labeled. In the present work, we design a
benchmark that emphasizes few-shot adaptability.
Although our dataset is limited to four regions and
two languages, the few-shot setup and high degree
of linguistic dissimilarity between the selected

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of translators, using the MQM protocol (Freitag
et coll., 2021un) that we also employ to evaluate
system translation quality.

3.1 Data Sampling Method

FRMT seeks to capture region-specific linguistic
differences, as well as potential distractors. À
this end, we divide the dataset into three buckets
(lexical, entity, random), each containing
human translations of sentences extracted from
different sets of English Wikipedia articles.1

Lexical: We collect English lexical items for
which the best translation into the target language
differs depending on the target region. To source
ces, we rely on blogs and educational websites
that list terms differing by region. We further
validate each pair of translations by asking a na-
tive speaker of each region whether each trans-
lation is appropriate for the intended meaning in
their region. We filter to only use pairs where
exactly one translation is appropriate per region.
This is done independently for Portuguese and
Mandarin as target languages, yielding lists of
23 et 15 termes, respectivement. For each term t,
we extract up to 100 sentences from the begin-
ning of the English Wikipedia article with title t.

Entity: We select entities that are strongly
associated with specific regions under consider-
ation (par exemple., Lisbon and S˜ao Paulo), which may
have adversarial effects for models that rely
heavily on correlations learned from pretraining.
Our selection comprises 38 Mandarin-focused
et 34 Portuguese-focused entities. We extract
up to 100 source sentences from the beginning of
the English Wikipedia article about each selected
entity.

Random: For a more naturally distributed
subset, we randomly sample 100 articles from
Wikipedia’s collections of ‘‘featured’’ or ‘‘good’’
articles.2 Here, we take up to 20 sentences from
the start of a randomly chosen section within
each article. Unlike the other two buckets, ce
one features one common set of sentences to be
translated into all four target variants.

1As Wikipedia data source we use the training split of
wiki40b (v1.3.0) by Guo et al. (2020), availableat https://
www.tensorflow.org/datasets/catalog/wiki40b.

2https://en.wikipedia.org/wiki/Wikipedia
:Good articles/all and https://en.wikipedia
.org/wiki/Wikipedia:Featured articles (as of
2021-12-15).

Bucket

Split

Portuguese Mandarin

Lexical

Entity

Random

Total

Exemplar
Dev
Test

118
848
874

112
935
985

111
744
757

341
2527
2616

173
524
538

104
883
932

111
744
757

388
2151
2227

Tableau 1: Number of sentence pairs by bucket,
split, and language, as well as cross-bucket totals.
Note, the random bucket contains the same En-
glish source sentences across the Portuguese and
Mandarin sets.

3.2 Human Translation

Fourteen paid professionals translated the selected
English texts into the four target language vari-
ants: 3 translators per Portuguese region and 4 par
Mandarin region. For each region, each sentence
was translated by one translator, resulting in one
reference per source. Each translator translated
non-overlapping chunks of the source data one
sentence at a time in the order of the original
text. Sentences that were rejected by at least one
translator (par exemple., for having too much non-English
text) are not included in our dataset.

3.3 Corpus Statistics

For each bucket, we split our data into exemplar,
development (dev), and test data. The exemplars
are intended to be the only pairs where the re-
gion label is shown to the model, such as via few-
shot or in-context learning (Brown et al., 2020).
Providing these ensures increased comparability
across methods on the FRMT benchmark, in ad-
dition to sidestepping potential domain mismatch
issues by providing exemplars from the same
domain (Wikipedia text) as the evaluation data.

Tableau 1 reports the number of released sen-
tence pairs for each split of the dataset. Sentences
from a given Wikipedia page appear only in a
single split, ensuring a system cannot ‘‘cheat’’ by
memorizing word–region associations from the

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Bucket

lexical

pt-BR

pt-PT

Em 2019, a Virgin Atlantic comec¸ou a permitir
que suas comiss´arias de bordo femininas usassem
calc¸as e n˜ao usassem maquiagem.

Em 2019, a Virgin Atlantic comec¸ou a autorizar
as assistentes de bordo a usar calc¸as e a dispensar
maquilhagem.

Dans 2019, Virgin Atlantic began to allow its female flight attendants to wear pants and not wear makeup.

entity

Os ˆonibus s˜ao o meio mais barato de se movimentar
por Natal.

Os autocarros s˜ao a maneira mais barata de viajar
pelas localidades pr´oximas de Natal.

Buses are the cheapest way to move around Natal.

random

O suco causa alucinac¸ ˜oes psicod´elicas intensas
em quem o bebe, e a pol´ıcia logo o rastreou at´e
a fazenda e partiu para prender Homer, Seth e
Munchie.

O sumo provoca fortes alucinac¸ ˜oes psicad´elicas
a quem bebe do mesmo e a pol´ıcia rapidamente
segue o rasto at´e `a quinta, deslocando-se at´e l´a para
prender Homer, Seth e Munchie.

The juice causes intense psychedelic hallucinations in those who drink it, and the police quickly trace
it to the farm and move in to arrest Homer, Seth, and Munchie.

Tableau 2: Examples from the dataset, limited to the Portuguese dev-set for brevity. The last two columns
show the reference human translations obtained for each region given the English source text (in italics).
For the lexical and entity buckets, we show examples for which the Levenshtein edit-distance between
the two translations is near the median observed for the whole dev-set.

exemplars, or by overfitting to words and entities
while hill-climbing on the dev set.

Tableau 2 shows example items from each bucket.

3.4 Limitations

Our dataset is designed to capture differences in
regional varieties, but capturing all such differ-
ences in a finite dataset is impossible. While we
specifically target lexical differences, the terms
were selected via a manual process based on on-
line resources that discuss lexical differences in
these languages, and these resources can some-
times be incorrect, outdated, or inattentive to rare
words or words with more subtle differences.
Other regional distinctions, such as grammatical
differences, were not specifically targeted by our
data bucketing process, and thus the degree to
which they are captured by the dataset is deter-
mined by their likelihood to occur in translations
of English Wikipedia text. This also means that
differences that only surface in informal settings
are unlikely to be included, as Wikipedia text has
a generally formal style.

While we believe that methods that perform
well on all four varieties included in FRMT should
be applicable to other languages and varieties,
measuring this would require a similar dataset
with wider coverage. Constructing such a dataset
requires only knowledge of regional differences
to inform selection of source texts as in our lex-
ical and entity buckets, and translators who

are native speakers of the target varieties. An ad-
ditional pool of MQM-trained translators would
be needed to validate the collected translations for
regional distinctiveness.

In spite of validation through MQM, it should
be noted that
the region-targeted translations
we collected are not necessarily minimal con-
trastive pairs, but may include differences arising
from factors other than regional variation, tel
as individual style preferences of the human
translators.

4 Evaluation Metrics

While human judgments are the gold standard
for evaluating machine-generated texts, collect-
ing them can be time-consuming and expensive.
For faster iteration, it can be helpful to mea-
sure progress against automatic metrics that are
known to correlate well with human judgments.
We hypothesize that common reference-based MT
evaluation metrics might have differing sensitivi-
ties to regional differences, and so we conduct a
human evaluation of several baseline models (voir
§6.1) and compute correlation of several auto-
matic metrics with the human judgments. We also
propose a new automated lexical accuracy metric
that more directly targets region-awareness.

4.1 Human Evaluation

To obtain the highest fidelity human ratings, nous
use the expert-based Multidimensional Quality

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Metrics (MQM) evaluation framework proposed
by Freitag et al. (2021un) and recommended by
the WMT’21 Evaluation Campaign (Freitag et al.,
2021b). We show expert raters chunks of 10 con-
tiguous English sentences from our test set with
one corresponding set of translations. Raters then
identify errors in the translations, assigning a cate-
gory and severity to each. Due to cost constraints,
we evaluate 25% of the test set, evenly distributed
across our three evaluation buckets. Within each
region, each chunk is rated by three raters, OMS
achieve interannotator consistency of 70.4 ± 2.2
(as 100-scaled intraclass correlation3).

Each output is shown to raters of both regions
of the corresponding language. All Mandarin out-
puts are automatically converted into the rater’s
region’s corresponding Han script (Mainland:
simplified; Taiwan:
traditional), using Google
Translate ‘‘Chinese (Simplified)’’ ↔ ‘‘Chinese
(Traditional)’’, which as of March 2023 converts
between these regions using only basic script
conversion rules.

4.2 Automatic Translation Quality Metrics

We evaluate the following automatic, reference-
based metrics:

BLEU (Papineni et al., 2002): Based on token
n-grams, using corpus bleu from Post (2018).4
CHRF (Popovi´c, 2015): Based on charac-
ter n-gram F1, using corpus chrf from Post
(2018).5

BLEURT (Sellam et al., 2020): A learned,
model-based metric that has good correlation with
human judgments of translation quality. To the
best of our knowledge, BLEURT has not been
evaluated with respect to human judgments of
region-specific translation quality.

BLEURT-D{3,6,12} (Sellam et al., 2020):
These distilled versions of BLEURT are less
resource-intensive to run, and have 3, 6, et 12
layers, respectivement. For all BLEURT variants, nous
use checkpoints released by its authors.

As in the human evaluation, all Mandarin out-
puts are converted into the target regional Han
script before evaluation.

3Using the icc function of R’s irr library (Gamer

et coll., 2019).

4SacreBLEU version strings for {Portuguese,Mandarin}:

BLEU|nrefs:1|case:mixed|eff:Non|tok:{13un,zh}|lisse:exp|version:
2.3.1.

5SacreBLEU version string:

Metric

Kendall’s τ

Pearson’s ρ

CHRF
BLEU
BLEURT-D3
BLEURT-D6
BLEURT-D12
BLEURT

43.6
44.9
50.6
50.7
51.2
52.4

48.4
57.5
63.1
63.3
64.0
65.4

Tableau 3: Coefficients of correlation between hu-
man MQM ratings and several automated metrics.
CHRF has the lowest correlation, with BLEU per-
forming slightly better. All BLEURT models out-
perform the non-learned metrics, with the full-size
model achieving higher correlation than the
smaller distillations thereof.

4.3 Correlation

For computing correlation, each data point is a
score on a 10-sentence chunk of model output,
covering the three models discussed in section
§6.1, using both matched and mismatched ratings.
For MQM, this is the average of 30 weighted
ratings: one per sentence per rater. The category/
severity weights are described in Freitag et al.
(2021un). For BLEU and CHRF, which are corpus-
level metrics, we take the 10 input/output sen-
tence pairs as the ‘‘corpus’’. For BLEURT, nous
use the average sentence-level score. Tableau 3
presents the correlation results, scaled by −100.6
We observe that the learned BLEURT met-
rics outperform the non-learned metrics by wide
margins, in line with findings from Sellam et al.
(2020) that neural methods outperform n-gram
based methods. En plus, the teacher model
(BLEURT) outperforms the distilled student mod-
le, with larger students consistently outperform-
ing smaller ones.

4.4 Lexical Accuracy

To assess a model’s ability to select lexical forms
appropriate to the target region, we define a lex-
ical accuracy metric. As discussed in section
§3.1, sentences in the lexical bucket are from
Wikipedia articles containing specific words that
we expect to have distinct regional translations.
Par exemple, we include source sentences from
the English Wikipedia article ‘‘Bus’’ in the Por-
tuguese lexical bucket, as the word for bus is

6We negate the correlations with MQM because higher

quality corresponds to lower MQM scores.

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distinct in Brazil and Portugal (ˆonibus vs. auto-
carro). As the expected output words are known
ahead of time, we can directly measure the rate at
which a model selects region-appropriate variants.
Starting from the list of terms used to select
articles for the lexical bucket, we remove the
terms selected for the exemplars split in order to
test generalization to unseen terms. This results in
18 term-pairs in Portuguese and 13 in Mandarin.
We calculate the metric over all model outputs
for the lexical bucket, covering both regions.
For each term-pair, we calculate the number of
sentences containing the matched variant and the
number of sentences containing the mismatched
variant. The model’s lexical accuracy (LA) for the
given language is then the total number of matches
divided by the sum of matches and mismatches:

LA =

Nmatch
Nmatch + Nmismatch

(1)

To account for Portuguese inflection, we con-
sidered matching lemmatized forms rather than
surface forms, but found little difference in the re-
sulting scores. We thus report results using naive
surface matching, which avoids a dependency on a
specific lemmatizer and improves reproducibility.
To disentangle lexical choice from script
choice, we define lexical accuracy to be script-
agnostic—e.g., for the word pineapple, if the tar-
get is zh-TW, we count both script forms of the
Taiwan variant f`engl´ı (
) as cor-
rect, and both script forms of the Mainland variant
b¯olu´o (
) as incorrect. This ensures
that models are judged solely on their lexical
choices, and prevents ‘‘gaming’’ the metric by
only using the lexical forms and script of a sin-
gle region.

We emphasize that lexical choice is just one
important facet of region-aware translation, aside
from morphology, syntax, et au-delà. Even so,
we believe that this easy-to-calculate metric is
worth iterating on, since one may safely say that
a model that scores poorly on lexical accuracy
has not solved region-aware translation.

4.5 Reporting FRMT Results

entraînement, except for data from the FRMT exem-
plars split. This restriction covers both region-
labeled monolingual data as well as region-labeled
parallel translation data.7 While it may not be dif-
ficult to obtain region labels for Brazil/Portugal
or Mainland/Taiwan (par exemple., by filtering web pages
on top-level web domain), we intend for FRMT
to serve as a measure of few-shot generalization
to arbitrary regions and language varieties, pour
which obtaining labels may be much harder.

Researchers sharing FRMT results should re-
port lexical accuracy, per-bucket BLEU, et
the ‘‘FRMT’’ score (described in §6.2) on test,
as shown in Tables 4 et 5. These metrics can be
calculated with our provided evaluation scripts.8
We also recommend reporting BLEURT scores,
but recognize that this may not always be pos-
sible, as it requires significantly more computa-
tional resources. De la même manière, we encourage human
evaluation using MQM as a gold standard, but do
not wish to promote this as a community metric,
due to its impracticality for many researchers
and the potential confound of having different
rater pools.

Enfin, for any model candidate, it is impor-
tant to report how many exemplars were sup-
plied for each variety. To improve comparability,
we recommend 0, 10, ou 100 exemplars per region.

5 Baseline Models

We evaluate a handful of academic MT mod-
els that claim some ability to provide few-shot
controllable translations. We also evaluate a com-
mercial MT system that does not distinguish be-
tween these regional varieties.

Our first baseline is the Universal Rewriter
(UR) of Garcia et al. (2021), which supports
multilingual style transfer and translation. It is ini-
tialized from an mT5-XL checkpoint (Xue et al.,
2021) and finetuned on a combination of mono-
lingual and parallel data from mC4 and OPUS,
respectivement. We train it with sequence length of
128 instead of 64, to be directly comparable to
our other models.

For the FRMT task (as opposed to the dataset),
we stipulate a key ‘‘few-shot’’ restriction: candi-
date models may not be intentionally exposed
to any region-labeled data at any point during

7Models may train on multilingual web crawl data, comme
is common practice, as long as supervised region labels are
not provided. We allow that some implicit or explicit region
labels may appear by chance within the unsupervised data.

8Scripts available at https://bit.ly/frmt-task.

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Model

M4-UR

Lexical

Entity

Random

pt-BR

pt-PT

pt-BR

pt-PT

pt-BR

pt-PT

FRMT

37.4 (69.9)

32.7 (68.0)

46.7 (76.3)

40.8 (73.6)

39.8 (70.7)

35.3 (69.2)

38.7 (71.3)

46.7 (74.5)

32.7 (69.7)

53.5 (79.9)

45.4 (77.5)

43.1 (70.9)

32.9 (68.4)

42.0 (73.5)

M4-Prompts

54.1 (77.1)

36.9 (72.1)

56.9 (81.1)

47.3 (78.4)

56.1 (77.5)

41.0 (73.7)

48.2 (76.6)

M4-Prompts FT

45.5 (70.1)

32.5 (67.4)

48.6 (73.8)

40.7 (72.8)

48.1 (70.5)

36.9 (69.0)

41.7 (70.6)

PaLM 8B

PaLM 62B

38.6 (69.8)

26.7 (65.8)

45.9 (75.9)

38.0 (73.6)

39.3 (69.4)

32.1 (67.8)

36.5 (70.4)

49.5 (75.9)

36.7 (72.4)

55.4 (80.1)

46.1 (77.8)

50.3 (75.2)

41.5 (73.5)

46.3 (75.8)

PaLM 540B

53.7 (77.1)

40.1 (73.9)

59.0 (81.2)

49.5 (79.0)

54.8 (76.9)

45.6 (75.5)

50.2 (77.3)

Google Translate

56.2 (78.7)

35.6 (72.3)

56.3 (81.2)

46.9 (78.3)

65.2 (80.5)

42.9 (75.0)

49.8 (77.6)

zh-CN

zh-TW

zh-CN

zh-TW

zh-CN

zh-TW

M4-UR

22.6 (58.5)

13.8 (56.0)

26.7 (67.1)

19.5 (65.3)

26.4 (62.1)

20.4 (61.0)

21.3 (61.7)

33.3 (65.0)

18.9 (58.2)

43.2 (73.0)

31.4 (70.4)

40.8 (65.4)

30.8 (63.6)

32.5 (65.9)

M4-Prompts

33.3 (64.9)

18.3 (57.6)

44.2 (72.5)

32.0 (68.7)

43.7 (67.0)

32.2 (63.4)

33.3 (65.6)

M4-Prompts FT

33.8 (65.7)

18.8 (59.0)

44.8 (73.2)

31.6 (69.8)

42.7 (66.7)

31.5 (64.0)

33.2 (66.4)

PaLM 8B

PaLM 62B

17.6 (55.7)

13.3 (52.3)

28.1 (65.7)

24.4 (63.9)

21.6 (56.3)

18.2 (56.1)

20.4 (58.3)

29.2 (62.2)

20.4 (59.8)

40.2 (71.8)

33.0 (69.9)

34.5 (64.0)

26.0 (63.1)

30.3 (65.1)

PaLM 540B

34.8 (66.5)

24.6 (63.3)

44.9 (74.7)

35.2 (72.5)

40.0 (67.8)

29.6 (66.0)

34.5 (68.4)

Google Translate

39.7 (68.0)

21.9 (61.8)

50.4 (75.0)

37.0 (72.2)

56.1 (72.0)

39.9 (68.7)

40.1 (69.6)

Tableau 4: FRMT per-bucket test set results, in the format: BLEU (BLEURT). The ‘‘FRMT’’ score is the
geometric mean across regions of the arithmetic mean across buckets.

Model

Gold

UR
M4-UR
M4-Prompts
M4-Prompts FT
PaLM 8B
PaLM 62B
PaLM 540B
Google Translate

98.6

50.4
51.2
66.7
66.7
85.0
90.4
93.2
50.0

94.4

50.6
50.9
50.0
51.0
69.0
70.8
83.6
50.0

Tableau 5: Lexical accuracy on FRMT test.
PaLM outperforms other approaches, while region-
agnostic models like Google Translate are guar-
anteed 50%.

Our second baseline is UR finetuned from the
Massively Multilingual Massive Machine transla-
tion (M4) model of Siddhant et al. (2022) instead
of mT5 (M4-UR). We hypothesize that initializ-
ing from a model explicitly designed for trans-
lation will outperform one trained as a general
language model. For both UR and M4-UR, nous
use the first 100 exemplars from the lexical
buckets.

Notre

third baseline uses natural

langue
prompting to control the regional variety of M4’s
output (M4-Prompts), such as prefixing the input
with ‘‘A Brazilian would write it like this:’’. Ce
is motivated by earlier work using this technique
effectively for large language models (Wei et al.,
2022; Sanh et al., 2022; Brown et al., 2020), et
more recent work applying it to region-aware MT
(Garcia and Firat, 2022).

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Our fourth baseline finetunes the M4-Prompts
model, where the source-side language tags used
to induce the target language are replaced with
prompts of the form ‘‘Translate to [langue]:’’.
This model (M4-Prompts FT) is designed to
explicitly introduce prompting behavior. At infer-
ence time, we replace ‘‘[langue]’’ with the vari-
ety name (par exemple., ‘‘Brazilian Portuguese’’). Neither
M4-Prompts nor M4-Prompts FT use exemplars.
Our next three baselines are different-sized ver-
sions of PaLM (Chowdhery et al., 2022), a large
language model that has demonstrated remark-
able zero-shot and few-shot performance on a
variety of tasks (PaLM 540B, PaLM 62B, et
PaLM 8B, referring to their approximate param-
eter counts). The prompt for these models begins
with ‘‘Translate the following texts from English
à [language variety]’’ and is followed by ten

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Chiffre 2: MQM (↓) scores for gold translations and model predictions in Portuguese (gauche) and Mandarin (droite).
Thick ‘‘match’’ bars show scores from raters in the target region. Thin ‘‘mismatch’’ bars show scores from raters
in the opposite region. In all conditions, raters prefer region-matched gold translations, confirming the presence
of region-specific phenomena in the collected data. PaLM is the highest-rated baseline, but still has room for
improvement, particularly in Mandarin.

exemplars selected randomly from the lexical
bucket.9 Each exemplar is put on two lines: d'abord
the English text, prefixed by ‘‘English:’’, et
then the translation in the target variety, prefixed
by the variety’s name. At the end of the prompt,
we show the model the input text and the lan-
guage variety prefix, and take the first decoded
line of text.

Enfin, we examine Google Translate,10 un
publicly available commercial MT model that
does not support regional varieties for Portuguese
or Mandarin (though it does support conversion
between traditional and simplified scripts). Nous
evaluate this system mainly to test the hypothesis
that variety-agnostic systems will be biased to-
ward the web-majority variety.

6 Baseline Model Performance

6.1 Human Evaluation Results

We select three baseline models for human eval-
uation: M4-UR, M4-Prompts, and PaLM 540B,
covering a variety of modeling techniques.

Chiffre 2 presents human evaluation of our
baselines on the 25% sample of our test set de-
scribed in §4.2. For the gold data, we observe
that raters of all regions prefer translations from
their own region (the ‘‘matched’’ case) over

9The model has a fixed input sequence length, y compris
the prompt, and a fixed output sequence length. We ensure
that the ten exemplars are short enough to leave at least 128
tokens for the input text, to match the 128 tokens allotted
to the output.

10https://translate.google.com, accessed April

4, 2022.

translations from the other region (the ‘‘mis-
matched’’ case) in all three buckets; when aver-
aged over buckets, the MQM penalties for the
matched and mismatched cases are significantly
different (1.73 matched and 3.55 mismatched;
t = −3.34; p < 0.001). This indicates that, de- spite the limitations discussed in §3.4, our data collection process succeeded in producing region- ally distinct translations. This effect is strongest in the lexical bucket, presumably due to the high rate of region-distinct terms in these sentences. In Portuguese, we find that all models perform better in the region-matched setting, indicating that each model has some ability to localize to Brazil and Portugal. However, in Mandarin, apart from PaLM’s lexical bucket, region match does not indicating that these models are not able to produce better, more region-specific translations in this case. lead to MQM gains, Comparing across models, we find that PaLM performs the best, followed by M4-Prompts and then M4-UR, consistently across both Portuguese and Mandarin. PaLM performs particularly well in the lexical bucket, suggesting that larger mod- els may be better suited to the task of memorizing region-specific lexical variants. For Mandarin, a large gap remains between ex- pert translations and our baselines: Averaged over buckets, the gold matched MQM penalty is 2.5 vs. PaLM’s 8.8. It’s apparent that better region handling will be needed to close this gap, since our baselines have much worse match/mismatch deltas than gold translations: The average gold mismatched penalty minus matched penalty was 2.7, while PaLM’s was −0.3. 678 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 5 6 8 2 1 4 1 0 1 5 / / t l a c _ a _ 0 0 5 6 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 As mentioned at the outset, we observe that region-agnostic models have a strong bias toward the region with larger presence in web-crawled corpora. This is especially apparent in the lexi- cal bucket, where Google Translate has a +20.6 BLEU gap between pt-BR and pt-PT and a +17.8 gap between zh-CN and zh-TW. Within the lexical bucket, we note that PaLM outperforms the public Google Translate model in web-minority regions (pt-PT and zh- TW) despite being trained in a fully unsupervised manner. This highlights that even with minimal region-labeled data (10 exemplars), it is possible to make meaningful progress over region-agnostic approaches. Table 5 shows lexical accuracy performance, assessing whether specific terms receive region- appropriate translations. Here, the PaLM models outperform alternatives by a wide margin. As even the smallest PaLM model has more than 2× the parameters of our other baselines (3.7B parame- ters each), this suggests that model capacity is a key ingredient for learning to use region-specific terminology in a few-shot manner. Still, there is a wide gap compared to human performance. Notably, while the smaller PaLM models out- perform our UR and M4 baselines on lexical accu- racy, they underperform on BLEU and BLEURT. This highlights that using region-appropriate ter- minology is only a small part of the translation task, and at smaller sizes, models designed spe- cifically for translation have the advantage. 6.3 Mismatched Outputs Given a reference in a specified language variety (e.g., pt-PT), a ‘‘good’’ model should achieve a higher score when translating into that variety (the ‘‘matched’’ case) than an alternative variety (e.g., pt-BR; the ‘‘mismatched’’ case). To measure the extent to which this holds for our baseline models, we show the delta between matched and mismatched outputs on the test set in Table 6. We observe that in the Portuguese case, most models do score better when asked to produce text in the same regional variety as the refer- ence. However, when it comes to Mandarin, most models—PaLM being the exception—struggle to produce zh-TW output that outperforms their zh-CN output when evaluated against a zh-TW reference, indicating that the attempts to appro- priately stylize the generated text degrade its Figure 3: MQM (↓) scores for gold translations and model predictions, broken down by rater region and target region. For example ‘‘BR rates PT’’ indicates Brazilian raters scoring sentences targeted to Portugal. For Portuguese, while PaLM gives impressive results, there is still a meaningful gap with ex- pert translation: Averaged over buckets, the gold MQM penalty was 2.1 vs. PaLM’s 2.7, indicating headroom for our task. There is also the impor- tant question of whether competitive performance can be achieved with smaller models, which are better suited for production use-cases. Figure 3 breaks down scores by rater and tar- get region, over the full 25% sample. As before, in each setting, raters prefer region-matched over mismatched gold translations. For Portuguese, we find that our pt-PT raters were ‘‘harder graders’’ than our pt-BR raters, with a delta of +2 MQM between the regions in both matched and mis- matched settings; by contrast, our Mandarin raters were well calibrated across regions. We further examined model performance on the entity bucket, to test whether the pres- ence of ‘‘distractor’’ entities (associated with the non-target region) would hurt translation qual- ity, but we did not find significant differences in MQM scores. Still, we note isolated examples of this effect; for instance, when targeting pt-BR, the M4-Prompts model produces the pt-PT spelling patrim´onio (cf. pt-BR patrimˆonio), but only when the English source contains the words Lisbon or Portugal. We expect the entity bucket will be useful to researchers looking for similar effects. 6.2 Automated Metric Results Table 4 shows performance of our baseline mod- els on the automated metrics BLEU and BLEURT. ‘‘FRMT’’ score is a summary of per-language performance, calculated as the geometric mean across regions of the arithmetic mean across buckets. 679 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 5 6 8 2 1 4 1 0 1 5 / / t l a c _ a _ 0 0 5 6 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Lexical Entity Random ΔFRMT Model UR M4-UR M4-Prompts M4-Prompts FT PaLM 8B PaLM 62B PaLM 540B UR M4-UR M4-Prompts pt-BR 1.3 (1.0) 1.0 (−0.2) 3.6 (1.9) 3.2 (−0.1) 6.5 (2.2) 13.1 (4.0) 13.8 (4.1) zh-CN 1.0 (−0.1) −0.1 (−0.3) 0.6 (1.6) M4-Prompts FT 1.5 (1.0) PaLM 8B PaLM 62B PaLM 540B 2.0 (1.1) 5.9 (1.7) 9.8 (4.2) pt-PT −0.2 (−0.8) −0.1 (0.4) 2.2 (0.7) 1.9 (2.2) 1.7 (1.0) 5.2 (2.7) 7.0 (3.2) zh-TW −0.4 (0.2) 0.2 (0.3) −0.5 (−1.8) −0.7 (−0.9) 1.9 (0.7) 3.5 (1.5) 4.7 (1.8) pt-BR 1.0 (0.8) 0.2 (0.0) 1.8 (0.5) 1.5 (−1.0) 4.6 (0.8) 9.6 (1.7) 9.7 (1.7) zh-CN 1.0 (0.5) 0.3 (0.1) 1.3 (1.2) 2.0 (0.8) 3.0 (1.0) 4.3 (0.8) 6.4 (1.4) pt-PT −1.0 (−0.8) 0.1 (0.1) 0.9 (0.2) 0.8 (1.4) 0.7 (0.4) 2.2 (1.1) 4.0 (1.5) zh-TW −0.3 (−0.4) 0.0 (−0.1) −0.5 (−1.2) −1.2 (−0.8) 0.2 (−0.9) 1.2 (0.0) 0.5 (0.0) pt-BR 1.5 (0.8) 0.9 (−0.2) 2.4 (1.2) 2.0 (−0.7) 4.3 (0.9) 8.0 (1.2) 9.1 (1.4) zh-CN 1.8 (1.1) −0.1 (−0.1) 1.3 (2.0) 1.6 (1.0) 2.4 (0.4) 5.9 (1.1) 9.0 (2.0) pt-PT −0.7 (−0.7) −0.5 (0.4) 0.5 (−0.4) 0.5 (1.3) 0.5 (0.1) 2.7 (0.9) 3.9 (1.5) zh-TW −0.9 (−0.8) −0.1 (0.2) −0.7 (−1.4) −1.2 (−1.1) 1.1 (0.2) 0.2 (0.6) −0.5 (0.4) pt 0.3 (0.0) 0.2 (0.1) 1.9 (0.6) 1.6 (0.5) 2.8 (0.9) 6.5 (1.9) 7.7 (2.2) zh 0.2 (0.1) 0.0 (0.0) 0.1 (0.0) 0.1 (0.0) 1.7 (0.4) 3.3 (0.9) 4.7 (1.6) Table 6: FRMT test set deltas between matched and mismatched outputs for a given reference, shown in the format: ΔBLEU (ΔBLEURT). Negative numbers indicate that the reference-based metric pre- ferred the model output that targeted the opposite language variety. The last column shows deltas between FRMT scores evaluated with respect to matched vs. mismatched outputs. Exemplars pt-BR pt-PT pt-BR pt-PT pt-BR pt-PT Lexical Entity Random FRMT pt 0 1 5 7 10 0 1 5 7 10 50.7 (75.7) 35.6 (71.2) 56.4 (80.3) 47.4 (77.6) 53.0 (76.0) 42.4 (73.6) 47.2 (75.7) 52.0 (77.1) 39.7 (73.7) 57.0 (81.2) 49.1 (78.5) 54.5 (77.0) 45.1 (75.2) 49.3 (77.1) 53.2 (77.0) 40.0 (74.0) 58.5 (81.2) 48.6 (78.7) 54.8 (76.8) 45.2 (75.3) 49.8 (77.2) 53.5 (77.1) 40.0 (73.8) 58.6 (81.3) 48.8 (78.8) 55.2 (77.0) 45.8 (75.5) 50.0 (77.2) 53.7 (77.1) 40.1 (73.9) 59.0 (81.2) 49.5 (79.0) 54.8 (76.9) 45.6 (75.5) 50.2 (77.3) zh-CN zh-TW zh-CN zh-TW zh-CN zh-TW zh 32.7 (64.5) 22.2 (61.3) 40.3 (72.7) 32.8 (70.2) 38.7 (65.6) 29.0 (63.1) 32.3 (66.2) 35.1 (66.4) 24.6 (64.3) 43.7 (74.2) 35.2 (72.8) 39.9 (67.6) 31.1 (66.4) 34.6 (68.6) 35.1 (66.7) 25.0 (63.9) 44.7 (74.6) 35.3 (72.8) 40.0 (67.6) 31.8 (66.7) 35.0 (68.7) 35.4 (66.6) 25.3 (64.2) 45.3 (74.7) 34.9 (72.6) 40.7 (68.0) 30.5 (66.6) 35.0 (68.8) 34.8 (66.5) 24.6 (63.4) 44.8 (74.7) 35.2 (72.5) 40.0 (67.8) 29.6 (66.0) 34.5 (68.4) Table 7: FRMT test set results of PaLM 540B, when varying the number of exemplars, shown in the format: BLEU (BLEURT). Across both languages, even one exemplar is sufficient for strong results, and zero-shot performance is reasonably strong. Increasing to 10 exemplars in Portuguese or 7 exemplars in Mandarin gives marginal additional gains. Note that these results were not used to select the num- ber of exemplars for the PaLM 540B results reported elsewhere; this ablation was run afterward. quality more than they improve its regional acceptability. 6.4 Effect of Exemplars To test sensitivity to the number and choice of exemplars, we evaluate PaLM 540B while varying the set of exemplars used. Table 7 shows the effect of ablating the number of exemplars in the range 0–10. We observe that a single exemplar is sufficient to achieve strong results, using zero exemplars yields reasonably strong results, and gains from additional exemplars are marginal. To measure the variance in performance across exemplar choice, we re-run PaLM 540B evalua- tion three times each using either 1 or 10 exem- plars, resampling the exemplars on each run. We 680 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 5 6 8 2 1 4 1 0 1 5 / / t l a c _ a _ 0 0 5 6 8 p d . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Model Gold PaLM Target:pt-BR Target:pt-PT A legalizac¸ ˜ao do casamento entre pessoas do mesmo sexo em Portugal ocorreu em 17 de maio de 2010. O casamento entre pessoas do mesmo sexo foi legalizado em Portugal a 17 de maio de 2010. O casamento entre pessoas do mesmo sexo em Portugal foi legalizado em 17 de maio de 2010. O casamento entre pessoas do mesmo sexo em Portugal foi legalizado a 17 de Maio de 2010. M4-Prompts O casamento entre pessoas do mesmo sexo em Portugal foi legalizado em 17 de maio de 2010. O casamento entre pessoas do mesmo sexo em Portugal foi legalizado a 17 de maio de 2010. M4-UR O casamento homoafetivo em Portugal foi legalizado em 17 de Maio de 2010. O casamento homoafetivo em Portugal foi legalizado a 17 de Maio de 2010. Table 8: Gold and model outputs for the source: Same-sex marriage in Portugal was legalized on 17 May 2010. Phenomena of interest are bolded. Table 9: Gold and model outputs for the source: Not all software defects are caused by coding errors. Phenomena of interest are bolded, and region-specific errors are underlined and red. Note, M4-based model zh-TW outputs have been converted to traditional script, matching our evaluation setting. find that the choice of exemplar(s) has a relatively small effect—with 10 exemplars, the standard de- viations of FRMT-BLEU and FRMT-BLEURT across all four runs (including the original) were below 0.5 in each language, and with just 1 exem- plar, the standard deviations remained under 1.0. 6.5 Qualitative Analysis To provide additional insights on regional differ- ences and model behavior, we manually inspect dev set gold translations and model outputs, across the models sent to human evaluation. In both lan- guages, we observe regional differences beyond just the lexical items underlying our lexical bucket. For instance, in Table 8 and similar exam- ples, we find on phrases tend to be trans-
lated with differing prepositions—em in pt-BR
and a in pt-PT. As another example, in Table 9,
we observe both gold and PaLM outputs use the
(ch´engsh`ı, dans:program) only in zh-
term
TW when translating the phrase ‘‘coding errors’’.
In many cases, PaLM uses the expected region-
specific lexical forms, as already reflected in our
lexical accuracy metric. Par contre, we observe
the M4-based models are more prone to use

terms from the web-majority region (pt-BR and
zh-CN) irrespective of the target. Par exemple,
in Table 9, PaLM matches gold translations in
using the region-specific terms for software—
(ruˇantˇı)—
zh-CN:
while the M4-based models use the zh-CN term
).
throughout (simplified:

(ruˇanji`an), zh-TW:

, traditional:

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7 Conclusion

In this paper, we introduced FRMT, a new
benchmark for evaluating few-shot region-aware
machine translation. Our dataset covers 4 régions
of Portuguese and Mandarin, and enables fine-
grained comparison across region-matched and
mismatched conditions, and across different
classes of inputs (lexical, entity, random).
While we found the large-scale generalist
model PaLM 540B to show impressive few-shot
region control, there is still significant room for
improvement. None of the models we evaluated
match human performance, and the gap is par-
ticularly large in Mandarin. En plus, là
remains an open research question as to whether
robust few-shot regional control can be achieved
at more modest model scales.

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We are eager to see progress on FRMT, comme
methods that do well in this few-shot setting are
likely to be easily extensible to other regions and
modes. We anticipate that the flexibility to adapt
to new output styles in the absence of extensive
labeled data will be a key factor in making gen-
erative text models more useful, inclusive, et
equitable.

Remerciements

For helpful discussion and comments, we thank
Jacob Eisenstein, Noah Fiedel, Macduff Hughes,
and Mingfei Lau. For feedback around regional
differences, we thank Andre Araujo, Chung-Ching
Chang, Andreia Cunha, Filipe Gonc¸alves, Nuno
Guerreiro, Mandy Guo, Luis Miranda, Vitor
Rodrigues, and Linting Xue.

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