Dialogue State Tracking with Incremental Reasoning
Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua
School of Computing
National University of Singapore
{liaolizi.llz, yunshan.ma, wenqianglei}@gmail.com
lehonglong@u.nus.edu
chuats@comp.nus.edu.sg
Abstracto
Tracking dialogue states to better interpret
user goals and feed downstream policy
learning is a bottleneck in dialogue
management. Common practice has been
to treat it as a problem of classifying
dialogue content into a set of pre-defined
slot-value pairs, or generating values for
different slots given the dialogue history.
Both have limitations on considering
dependencies that occur on dialogues, y
are lacking of reasoning capabilities. Este
paper proposes to track dialogue states
gradually with reasoning over dialogue
turns with the help of
the back-end
datos. Empirical results demonstrate that
our method outperforms the state-of-the-
art methods in terms of
joint belief
accuracy for MultiWOZ 2.1, a large-scale
human–human dialogue dataset across
multiple domains.
1
Introducción
to monitor
Dialogue State Tracking (DST) usually works
as a core component
the user’s
intentional states (or belief states) and is cru-
cial for appropriate dialogue management. A
state in DST typically consists of a set of
dialogue acts and slot value pairs. Considerar
the task of restaurant reservation as shown in
Cifra 1. In each turn,
the user may inform
the agent of particular goals (p.ej. single one as
inform(food=Indian) or composed one as
inform(area=center,food=Jamaican)).
Such goals given during a turn are referred as
turn belief. The joint belief
is the set of accu-
mulated turn goals updated until the current turn,
which summarizes the information needed to
successfully maintain and finish the dialogue.
557
Traditionally, dialogue system is supported by
a domain ontology, which defines a collection
of slots and the values that each slot can take.
The aim of DST is to identify good features or
patrones, and map to entries such as specific slot-
value pairs in the ontology. It is often treated as
a classification problem. Por lo tanto, most efforts
center on (1) finding salient features: from hand-
crafted features (Wang and Lemon, 2013; Sol
et al., 2014a), semantic dictionaries (Henderson
et al., 2014b; Rastogi et al., 2017), to neural
network extracted features (Mrkˇsi´c et al., 2017);
o (2) investigating effective mappings: de
rule-based models (Sun et al., 2014b), generative
modelos (Thomson and Young, 2010; williams
and Young, 2007), to discriminative ones (Sotavento
and Eskenazi, 2013; Ren et al., 2018; Xie
et al., 2018). Por otro lado, some researchers
attack these methods’ over-dependence on domain
ontology. They perform DST in the absence of
a comprehensive domain ontology and handle
unknown slot values by generating words from
dialogue history or knowledge source (Rastogi
et al., 2017; Xu and Hu, 2018; Wu et al., 2019).
Sin embargo, the critical problem of modeling the
dependencies and reasoning over dialogue history
is not well researched. Many existing methods
work on turn level only, which takes in the cur-
rent turn utterance and outputs the corresponding
turn belief (Henderson et al., 2014b; Zilka and
Jurcicek, 2015; Rastogi et al., 2017; Xu and Hu,
2018). Compared to joint belief, la resultante
turn belief only reflects single turn informa-
ción, and thus is of less practical use. Por lo tanto,
the joint belief
more recent efforts target at
that summarizes the dialogue history. Generally
speaking, they accumulate turn beliefs by rules
((Mrkˇsi´c et al., 2017; Zhong et al., 2018); Nouri
and Hosseini-Asl, 2018) or model information
across turns via various recurrent neural networks
(RNNs) (Wen et al., 2017; Ramadan et al., 2018).
Transacciones de la Asociación de Lingüística Computacional, volumen. 9, páginas. 557–569, 2021. https://doi.org/10.1162/tacl a 00384
Editor de acciones: Wenjie (Maggie) li. Lote de envío: 7/2020; Lote de revisión: 1/2021; Publicado 5/2021.
C(cid:2) 2021 Asociación de Lingüística Computacional. Distribuido bajo CC-BY 4.0 licencia.
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provides valuable hints for it to reason about
user goals and update belief states. It is therefore
natural to construct a bipartite graph based on the
database where the entities and entity attributes are
the two groups of nodes; with edges connecting
them to express attribute belonging relation. Como
the example in Figure 1, the database does not
contain restaurant entity serving Jamaican food
and located in center area. Thus there would
be no two-hop path between these two nodes.
Existing methods like Wu et al. (2019) have to
understand it via system utterances, while a DST
reasoning over database would easily obtain such
clues explicitly.
en este documento, we propose to do reasoning over
turns and reasoning over database in Dialogue
State Tracking (ReDST) for task-oriented systems.
For reasoning over turns, we model dialogue
state tracking as a recursive process in which
the current joint belief relies on the generated
current turn belief and last joint belief. Motivated
by the limited length of single turn utterance
and the good performance of pre-trained BERT
(Devlin et al., 2019), we formalize the turn belief
prediction as a token and sequence classification
problema. It follows a multitask learning setting
with augmented utterance inputs. To integrate
resultados, an incremental
el ultimo
inference module is applied for more robust
belief updates. For reasoning over a database,
we abstract the back-end database as a bipartite
graph, and propagate extracted beliefs over the
graph to obtain more realistic dialogue states.
Contributions are summarized as:
turn belief
• We propose to rethink the dialogue state
tracking problem for task-oriented agents,
pointing out the need for proper reasoning
over turns and reasoning over back-end data.
• We represent the database into a bipartite
graph and perform belief propagation on
él, which enables the belief
a
gain insight on potential candidates and
detect conflicting requirements along the
conversation course.
tracker
augmented
• With the help from pre-trained Transformer
models working
corto
en
utterance for achieving more accurate turn
creencias, we incrementally infer joint belief
via reasoning in a turn by turn style and
outperform state-of-the-art methods by a
large margin.
Cifra 1: An example dialogue for illustration. Turn
belief labels are provided based on turn information,
while the joint belief captures most updated user
intention up to the current turn.
Although these RNN based methods model dia-
logue in turn by turn style, they usually feed
the whole turn utterance directly to the RNN,
which contains a large portion of noise, and result
in unsatisfactory performance (Liao et al., 2018;
Zhang et al., 2019b). More recently, hay
works that directly merge fixed window of past
turns (Perez and Liu, 2017; Wu et al., 2019) as new
input and achieve state-of-the-art performance
(Wu et al., 2019). Sin embargo, their capability of
modeling long-range dependencies and doing rea-
soning in the interactive dialogue process is rather
limitado. Por ejemplo, (Wu et al., 2019) performs
gated copy to generate slot values from dialogue
historia. Although certain turns of utterances are
exposed to the model, since the interactive signals
are lost when concatenating turns together, it fails
to do in-depth reasoning over turns.
such methods
Very recently, there is research starting to work
in turn-by-turn style with pre-trained models.
Generally speaking,
take the
previous turn’s belief state and the current turn
to generate new dialogue
utterances as input
estado (Chao and Lane, 2019; Kim y cols., 2020;
Chen et al., 2020). Sin embargo, there exists a long
ignored fact that as an agent’s central component,
the state tracker not only receives dialogue
history but also observes the back-end database
or knowledge base. Such an information source
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2 Trabajo relacionado
2.1 Dialogue State Tracking
A plethora of research has been focused on DST.
We briefly discuss them in general chronological
orden. At the early stage, traditional dialogue state
trackers combine semantic information extracted
by Language Understanding (LU) modules to
do DST (Williams and Young, 2007; williams,
2014). Such trackers accumulate errors from the
LU part and possibly suffer from information
loss of dialogue context. Subsequent word-based
(Henderson et al., 2014b; Zilka and Jurcicek,
2015) trackers thus forgo the LU part and directly
infer states using dialogue history. Hand-crafted
semantic dictionaries are utilized to hold all
key terms, rephrases and alternative mentions to
delexicalize for achieving generalization (Rastogi
et al., 2017).
Recientemente, most approaches for dialogue state
tracking rely on deep learning models (Wen et al.,
2017; Ramadan et al., 2018). (Mrkˇsi´c et al.,
2017)
leveraged pre-trained word vectors to
resolve lexical/morphological ambiguity. As it
treats slots independently that might result
en
missing relations among slots (Ouyang et al.,
2020), Zhong et al. (2018) proposed global mod-
ules to share parameters between estimators for
different slots. Similarmente, (Nouri and Hosseini-Asl
2018) used only one recurrent network with global
conditioning to reduce latency while preserving
actuación. En general, these methods represent
the dialogue state as a distribution over all candi-
date slot values that are defined in the ontology.
This is often solved as a classification or matching
problema. Sin embargo, these methods rely heavily
on a comprehensive ontology, which often might
not be available. Por lo tanto, Rastogi et al. (2017)
introduced a sophisticated candidate generation
estrategia, mientras (Perez and Liu, 2017) seguido
the general paradigm of machine reading and
proposed to solve it using an end-to-end memory
network. Xu and Hu (2018) utilized the pointer
network to extract slot values from utterances,
while Wu et al. (2019) integrated copy mechanism
to generate slot values.
Sin embargo,
these methods
tend to largely
ignore the dialogue logic and dependencies.
Por ejemplo,
inter-utterance information and
correlations between slot values have been shown
to be challenging, let alone the frequent goal
shifting of users. Como consecuencia, reasoning over
turns is sensible. We first aim to improve the
turn belief prediction, then model the joint belief
prediction as an updating process. Very recently,
we see such design leveraged by several works.
Por ejemplo, Chao and Lane (2019) leverage
BERT model to extract slot values for each turn,
then employ a rule-based update mechanism to
track dialogue states across turns. Ren et al. (2019)
encode previous dialogue state and current turn
utterances using Bi-LSTM, then hierarchically
decode domains, slots, and values one after
otro. Al mismo tiempo, Kim et al. (2020)
encode these inputs with BERT model while
predicting operation gates and generating possible
valores. Still, such methods largely ignore the fact
that as an agent, it has access to the back-end
data structure which can be leveraged to further
improve the performance of DST.
2.2 Incremental Reasoning
The ability to do reasoning over the dialogue
history is essential for dialogue state trackers.
En
the turn level, we aim to extract more
accurate slot values from user utterance with
the help of contextualized semantic inference.
Contextualized representation learning in NLP
dates back to Collobert and Weston (2008)
but has had a resurgence in the recent year.
Contextualized word vectors were pre-trained
using machine translation data and transferred
to text classification and QA tasks (McCann et al.,
2017). Más recientemente, BERT (Devlin et al., 2019)
employed Transformer layers (Vaswani et al.,
2017) with a masked language modeling objective
and achieved superior performance across various
tareas. In DST, we also observe a wide adoption
of such models (Shan et al., 2020; Liao et al.,
2021). Por ejemplo, Kim et al. (2020) and Heck
et al., (2020) adopted the pre-trained BERT as
base network. Hosseini-Asl et al. (2020) aplicado
the pre-trained GPT-2 (Alec et al., 2019) modelo
as the base network for dialogue state tracking.
At dialogue context level, since we perform
reasoning via belief propagation through graph,
our work is also related to a wide range of graph
reasoning studies. As a relatively early work,
the page-ranking algorithm (Page et al., 1999)
used a random walk with restart mechanism to
perform multi-hop reasoning. Almost at the same
tiempo, loopy belief propagation (Murphy et al.,
1999) was proposed to calculate the approximate
marginal probabilities of vertices in a graph based
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Cifra 2: The architecture of the proposed ReDST model, which comprises (a) a turn belief generator, (b) a
bipartite belief propagator, y (C) an incremental belief generator. The turn belief generator will predict values
for domain slot pairs. Together with the last joint belief, the beliefs will be aggregated via the bipartite belief
propagator based on the database structure. Then the incremental belief generator infers the final joint belief.
on partial information. En años recientes, investigación
on graph reasoning has moved to learn symbolic
inference rules from relational paths in the KG
(Xiong et al., 2017; Das et al., 2017). Under these
settings, a large number of entities and many
types of relationships are usually involved. En
DST, Chen et al. (2020) leveraged schema graphs
containing slot relations, but their method heavily
relied on a complete slot ontology. Zhou and
Pequeño (2019) incorporated a dynamically evolving
knowledge graph to explicitly learn relationships
slots. In our work, only the attribute-belonging
relations are captured, and the constructed graph
is simply a bipartite graph. We thus resort to
heuristic belief propagation on the bipartite graph
for reasoning. Further exploring more advanced
models are treated as our future work.
3 ReDST Model
The proposed ReDST model in Figure 2 consists
of three components: a turn belief generator,
a bipartite graph belief propagator, and an in-
cremental belief generator. Instead of predicting
the joint belief directly from dialogue history,
we perform two-stage inference: It first obtains
turn belief from augmented turn utterance via
transformer models. Entonces, it reasons over turn
belief and last joint belief with the help of the
bipartite graph propagation results. Based on this,
it incrementally infers the final joint belief.
To facilitate the model description in detail, nosotros
first introduce our mathematical notations here.
We define X = {(U1, R1), · · · (UT , RT )} como el
set of user utterance and system response pairs in
T turns of dialogue, and B = {B1, · · · , BT }
as the joint belief states at each turn. Mientras
Bt summarizes the dialogue history up to the
current turn t, we also model the turn belief Qt
that corresponds to the belief state of a specific
doblar (Ut, Rt), and denote Dt as the domain of
this specific turn. Following (Wu et al., 2019),
we design our state tracker to handle multiple
tareas. De este modo, each Bt or Qt consists of tuples
como (domain, slot, valor). Suppose there are K
diferente (domain, slot) pares en total, we denote
Yk as the true slot value for the k-th (domain, slot)
pair.
3.1 BERT-based Turn Belief Generator
Denoting Xt = (Ut, Rt) as the t-th turn utterance,
the goal of turn belief generator is to predict
accurate state for this specific utterance. A pesar de
the dialogue history X can accumulate in arbitrary
length, the turn utterance Xt is often relatively
in oftentimes. To utilize contextualized
corto
representation for extracting beliefs and enjoy
the good performance of pre-trained encoders,
560
we fine-tune BERT as our base network while
attaching the sequence classification and token
classification layers in a multitask learning setting.
The token classification task extracts specific
slot value spans. The sequence classification task
decides which domain the turn is talking about
and whether a specific (domain, slot) pair takes
the gate value like yes, No, doncare, ninguno, o
generate from token classification, Etcétera.
The model architecture of BERT is a multi-
layer bidirectional Transformer encoder based on
the original Transformer model (Vaswani et al.,
2017). The input representation is a concate-
nation of WordPiece embeddings (Wu et al.,
2016), positional embeddings, and the seg-
ment embedding. As we need to predict
el
values for each (domain, slot) pair, we aug-
the input sequence as follows. Suppose
mento
as Xt =
tenemos
the original utterance
x1, · · · , xN , the augmented utterance is then X (cid:3)
t=
[CLS], domain, slot, [SEP], x1, · · · , xN , [SEP].
The specific (domain, slot) works as queries to
extract the answer span. We denote the outputs of
BERT as H = h1, …, hN +5.1 The BERT model
is pre-trained with two strategies on large-scale
unlabeled text, eso es, masked language model and
next sentence prediction, which provide a power-
ful context-dependent sentence representation.
We use the hidden state h1 corresponding to
[CLS] as the aggregated sequence representation
to do the domain dt and gate zt classification:
dt = sof tmax(Wdm · (h1)t + bdm),
zt = sof tmax(Wgt · (h1)t + bgt)
where Wdm is trainable weight matrix and bdm
is the bias for domain classification. And Wgt is
trainable weight matrix and bgt is the bias for gate
clasificación.
For token classification, we feed the hidden
states of other tokens h2, · · · , hN +5 into a softmax
layer to classify over the token labels S, I, oh,
[SEP] por
together. For the former, the cross-entropy loss
Lsc is computed between the predicted d, z and
the true one-hot label ˆd, ˆz,
Lsc = −log(d · (ˆd)t ) − log(z · (ˆz)t ).
(2)
For the latter, we apply another cross-entropy
loss Ltc between each token label in the input
secuencia.
Ltc = −
norte +5(cid:2)
n=2
registro(en
· (ˆyn)t ).
(3)
We optimize the turn belief generator via a
weighted sum of these two loss functions as below
over all training samples:
Lturn = αLsc + βLtc.
(4)
3.1.1 Filter for Improving Efficiency
As in turn belief, most of the slots will get the
value not mentioned. To enhance the efficiency of
our model, we further design a gate mechanism
similar to Wu et al. (2019) to filter out such slots
primero, for which we can skip the generation process
and predict the value none directly. Aplicamos el
separate training objective as the cross entropy
loss computed between the predicted slot gate
pf ilter
as below:
s
and the true one-hot label qf ilter
s
Lf ilter = −log(pf ilter
s
· (qf ilter
s
)t ),
where for prediction, we calculate HXt =
fBERT (Xt) as contextualized word representa-
tions for turn utterance, and then apply query
attention to classify whether the slot should be
filtered,
η = Sof tmax(HXt
· (qs)t ),
pf ilter
s
= Sof tmax(Wf ilter · (ηT · HXt)t ).
Wf ilter is the weight matrix and qs is the [CLS]
position’s output from a BERT encoder for the
domain-slot query.
yn = sof tmax(Wtc · (hn)t + btc),
(1)
3.2 Joint Belief Reasoning
where Wtc is trainable weight matrix and btc is
the bias for token classification.
To jointly model the sequence classification
and token classification, we optimize their loss
1For ease of illustration, we ignore the WordPiece
separation effect on token numbers.
the turn level belief
Now we can predict
state for each turn. Intuitivamente, we can directly
apply our turn belief generator on concatenated
dialogue history to obtain the joint belief as
is hardly
in Wu et al. (2019). Sin embargo,
él
treating all
an optimal practice. First of all,
lose the
utterances as a long sequence will
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iterative character of dialogue, thus resulting in
information loss. Segundo, current models like
recurrent networks or Transformers are known
for not being able to model
the long-range
dependencies well. Long sequences introduce
el
difficulty to the modeling as well as
computational complexity of Transformers. El
WordPiece separation operation makes sequences
even longer. Por lo tanto, we simulate the dialogue
procedure as a recursive process where current
joint belief Bt relies on the last joint belief Bt−1
and the current turn belief Qt. Generally speaking,
we use Bt−1 and Qt to perform belief propagation
on the bipartite graph constructed based on the
back-end database to obtain credibility score for
each slot value pairs. Entonces, we do incremental
belief reasoning over the recursive process using
different methods.
3.2.1 Bipartite Graph Belief Propagator
As the central component for dialogue systems,
the dialogue state tracker has access to the back-
end database most of the time. In the course
of the task-oriented dialogue, the user and agent
interact with each other to reach the same stage of
information awareness regarding a specific task.
The user expresses requirements that, a menudo, son
hard to meet. The agent resorts to the back-end
database and responds accordingly. Then the user
would adjust their requirements to get the task
hecho. In most existing DSTs, the tracker has to
infer such adjustment requirements from dialogue
historia. With reasoning over the agent’s database,
we expect to harvest more accurate clues explicitly
for belief update.
Como consecuencia, we abstract
the database as
a bipartite graph G = (V, mi), where vertices
are partitioned into two groups: The entity set
Vent and attribute set Vattr, where V = Vent ∪
Vattr and Vent ∩ Vattr = φ. The entities within
Vent and Vattr are totally disconnected. Edges
link two vertices from each of Vent and Vattr,
representing the attribute belonging relationship.
During each turn, we first map the predicted Qt
and last joint belief Bt−1 to belief distributions
over the graph via the function g(·). Here we
apply fuzzy match and calculate the similarity
with a threshold (cid:5) to realize g(·). We use BERT
tokenizer to tokenize both dialogue and database
entradas. The mapping is done based on a pre-
set threshold on the token level overlap ratio. Para
ejemplo, the generated ‘cambridge punt ##er’ will
be mapped to the database entry ‘the cambridge
punt ##er’ when their overlap ratio is larger than
(cid:5). In our experiment, we find that approximately
60.5% of entity names and 12.2% other slot values
can be mapped.2 This mapping operation actually
helps to correct some minor errors made in span
extraction or generation.
After the mapping of beliefs to the database
bipartite graph via g(·), we start to do belief
propagation over the graph. Generally speaking,
there are two kinds of belief propagation in the
bipartite graph. The first is from Vent to Vattr.
It simulates the situation when a venue entity is
mentioned, its attributes will be activated. Para
ejemplo, after a restaurant is recommended, a
nearby hotel will have the same location value
with it. The second one is from Vattr to Vent.
This simulates the situation when an attribute is
mentioned, all entities having this attribute will
also receive the propagated beliefs. If an entity
gets more attributes mentioned, it will receive
more propagated beliefs. Suppose the propagation
result is ct for the current turn t, it can be viewed
as the credibility scores of the state values after
reasoning over the database graph. We reason over
this set of entries via doing belief propagation in
the bipartite graph to obtain the certainty scores
for them as below:
ct = γ · g(Bt−1) + η · g(Qt) · (I + Wadj),
(5)
where γ is a hyper-parameter for modeling the
credibility decay, because newly provided slot
values usually reflect more updated user intention.
η adjusts the effect of propagated beliefs. Wadj
is the adjacency matrix of the bipartite graph.
Note that the belief propagation method is rather
simple but effective. We tried more advanced
methods such as loopy belief propagation (Murphy
et al., 1999). Sin embargo, we did not see obvious
performance gain, which might be due to the
relatively small bipartite graph size (273 nodos
en total). También, we suspect that graph reasoning
might be more helpful for down stream tasks such
as action prediction. We will explore further in
future.
3.2.2 Incremental Belief Generator
With the credibility scores ct obtained from the
belief propagator, we now incrementally infer the
2Over half of the slot values are time, gente, stay, día,
etc.. There are no such nodes in the bipartite graph but we
keep these slot values’ existence in the belief vector
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current joint belief Bt. Mathematically, tenemos
Bt = f (Qt, Bt−1, ct).
(6)
The function f integrates evidence from the
turn belief, last joint belief, and the propagated
credibility scores. There are wide variety of
models that can be applied. We may leverage the
straight-forward Multi-Layer Perceptron (MLP) a
model the interactions between these beliefs (Él
et al., 2017) deeply. Due to the sequential nature
of the belief generator, we can also apply GRU
cells to predict the beliefs turn by turn (Cho et al.,
2014). Intuitivamente, given these remaining and new
belief entries as well as credibility scores, el
essential task here is to reason out what entries
to keep, update, or delete. Por lo tanto, we make
use of these information to carry out the operation
classification task. There are three operations keep,
update, and delete to choose from for each domain
slot. For the GRU case, the detailed equation for
operation classification is as below:
ht = GRU (W · [gramo(Qt), ct], ht−1)
opk = sof tmax(Wopk
· (ht)t + bopk ),
where W · [gramo(Qt), ct] and ht−1 are the inputs to the
GRU cell. [, ] denotes vector concatenation. Wopk
and bopk are the weight matrix and bias vector for
the corresponding k-th (domain, slot) pair. Después
the operation op in the current turn t is predicted,
we obtain the corresponding current joint belief
Bt via performing corresponding operations.
4 experimentos
4.1 Dataset
We carry out experiments on MultiWOZ 2.1
(Eric et al., 2019). It is a multi-domain dialogue
dataset spanning seven distinct domains and
containing over 10,000 dialogues. As compared to
MultiWOZ 2.0, it fixed substantial noisy dialogue
state annotations and dialogue utterances that
could negatively impact the performance of state-
tracking models. In MultiWOZ 2.1,
hay
30 domain-slot pairs and over 4,500 posible
valores, which is different from existing standard
datasets like WOZ (Wen et al., 2017) and DSTC2
(Henderson et al., 2014a), which have fewer than
ten slots and only a few hundred values. We follow
the original training, validation, and testing split
and directly use the DST labels. Since the hospital
and police domain have very few dialogues (10%
compared to others) and only appear in the training
colocar, we only use the other five domains in our
experimento.
4.2 Settings
Training Details Our model is trained in a two-
stage style. We first train the turn belief generator
using the Adam optimizer with a batch size of 32.
We adopt the bert-base-uncased version of BERT
and initialize the learning rate for fine-tuning as
3e-5. The α and β in Equation 4 are set to 0.05
y 1.0, respectivamente. We use the average of the
last four hidden layer outputs of BERT as the final
representation of each token.
During the later reasoning stage, regarding
incremental belief reasoning, we use a fully
connected two-layer feed-forward neural network
with ReLU activation for MLP. The hidden size
is set to 500, and the learning rate is initialized
como 0.002. For GRU, we set the learning rate as
0.005. We pre-process turn utterances to alleviate
the problem of ground truth absence, Por ejemplo,
formalize time values into standard forms. Similar
to Heck et al. (2020), we also make use of the
system acts to enrich the system utterances.
Evaluation Metrics Similar to Wu et al. (2019),
we adopt the evaluation metric joint goal accuracy
to evaluate the performance. It is a relatively
strict elevation standard. The joint goal accuracy
compares the predicted belief states to the ground
truth Bt at each turn t. The joint accuracy is 1.0 si
and only if all (domain, slot, valor) triplets are
predicted correctly at each turn, otherwise it is 0.
Baselines We denote the two versions of ReDST
con diferentes
incremental reasoning modules
as ReDST M LP , and ReDST GRU . Ellos son
compared with the following baselines.
DST Reader
(Gao et al., 2019): It treats DST
as a reading comprehension problem. Given the
historia, it learns to extract slot values as spans.
HyST (Goel et al., 2019):
It combines a
hierarchical encoder in a fixed vocabulary system
with an open vocabulary n-gram copy-based
sistema.
TRADE (Wu et al., 2019): It concatenates
the whole dialogue history as input and uses a
generative state tracker with a copy mechanism to
generate value for each slot separately.
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DST-Picklist
(Zhang et al., 2019a): Given the
whole dialogue history as input, it uses two BERT-
based encoders and takes a hybrid approach
of predefined ontology-based DST and open
vocabulary-based DST. It defines picklist-based
slots for classification and span-based slots for
span extraction like DSTRead (Gao et al., 2019).
SOM (Kim y cols., 2020): It works in turn-by-turn
style and considers state as an explicit fixed-sized
memory, and adopts a selectively overwriting
mechanism for generating values with copy.
SST (Chen et al., 2020): It leverages a graph
attention matching network to fuse information
from utterances and schema graphs. A recurrent
graph attention network controls state updating. Él
relies on a predefined ontology.
4.3 DST Results
We first compare our model with the state-of-the-
art methods. As shown in Table 1, we observe that
our method outperforms all the other baselines.
Por ejemplo, in terms of joint accuracy, cual
is a rather strict metric, ReDST GRU improves
the performance by 46.2%, 17.4%, y 1.3% como
compared to open-vocabulary based methods: el
DST Reader, TRADE, and SOM, respectivamente.
Based on results in Table 1, the methods such
as DST-Picklist and SST perform better than
our method. Sin embargo, they rely heavily on a
predefined ontology. In such methods, el valor
candidates for each slot to choose from are fixed
already. They cannot handle unknown slot values,
which largely limits their application in real-life
escenarios.
We observe that a large portion of baselines
work on relatively long window-sized dialogue
historia. FJST directly encodes the raw dialogue
history using recurrent neural networks. en contra-
contraste, HJST first encodes turn utterance to vectors
using a word-level RNN, and then encodes the
whole history to vectors using a context level
RNN. Sin embargo, the lower performance of HJST
demonstrates its inefficiency in learning useful
features in this task. Based on HJST, HyST man-
ages to achieve better performance by further
integrating a copy-based module. Still, the perfor-
mance is lower than TRADE, which encodes the
raw concatenated whole dialogue history, gener-
ates or copies slot values with extra slot gates.
Generally speaking, these baselines are based on
predefinido
ontology
abierto-
vocabulary
Modelo
FJST
HJST
HyST
DST-Picklist
SST
DST Reader
TRADE
TRADE w/o gate
SOM
ReDST M LP
ReDST GRU
Joint Acc
0.378
0.356
0.381
0.533
0.552
0.364
0.453
0.411
0.525
0.511
0.532
Mesa 1: The multi-domain DST evaluation
results on the MultiWOZ 2.1 conjunto de datos. El
ReDST GRU method achieves the highest
joint accuracy.
recurrent neural networks for encoding dialogue
historia. Since the interactions between user and
agent can be arbitrarily long and recurrent neural
networks are not effective in modeling long-range
dependencies, they might not be a good choice
to model the dialogue for DST. On the contrary,
single turn utterances usually are short and con-
tain relatively simple information as compared
to complicated dialogue history. It is thus better
to generate belief in turn level and then integrate
them via reasoning. According to the comparisons
of baselines, the superior performance of SST,
SOM, and ReDSTs validate this design.
Además, we also tested the performance of
TRADE without the slot gate. The performance
drops dramatically–from 0.453 a 0.411 in terms
of joint accuracy. We suspect that this is due
to lengthy dialogue history, where the decoder
and copy mechanism start to lose focus. It might
generate some value that appears in dialogue
history but is not the ground truth. Por lo tanto, el
slot gate is used to decide which slot value should
be taken, which resembles the inference in some
sense. To validate this, we feed the single turn
utterances to TRADE and generate the turn beliefs
as output. Curiosamente, we find that it performs
similar with gate or without it, which validates our
guess. Sin embargo, such resembled inference is not
suficiente. When the dialogue history becomes long,
the gating mechanism will lose its focus easily.
Respectivamente, we report the results of TRADE and
ReDST GRU on the last four turns of dialogues in
Mesa 2. The better performance of ReDST GRU
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Modelo
TRADE
ReDST GRU
T-3
0.411
0.487
T-2
0.339
0.440
T-1
0.269
0.391
t
0.282
0.377
Setting
ReDST M LP
ReDST GRU
w BP
0.511
0.532
w/o BP
0.507
0.530
Mesa 2: The last four turns’ joint accuracy of
TRADE and proposed ReDST. (T refers to the
last turn of each dialogue session.)
Mesa 4: The joint accuracy results for ReDST
methods with or without bipartite graph reasoning.
Modelo
TRADE
SOM
ReDST
Joint Acc
0.697
0.799
0.808
Mesa 3: The turn belief generation results of
TRADE, SOM, and proposed ReDST.
further validates the importance of reasoning over
turns. Usually, as the interactive dialogue goes
en, users might frequently adjust
their goals,
which requires special consideration. Since turn
utterance is relatively more straightforward and
dialogue is turn by turn in nature, doing DST turn
by turn is a useful and practical design.
4.4 Component Analysis
Since our model makes use of the advanced
BERT structure to learn the contextualized
representación, we first test how much contribution
the BERT has made. Por lo tanto, we carried out a
study on a turn belief generator and compare it
with SOM and the BiLSTM baseline TRADE on
the single turn utterance. As shown in Table 3, nosotros
observe that the BERT-based SOM and ReDST
indeed perform better than single turn TRADE.
This is due to the usage of pre-trained BERT
in learning better-contextualized features. En el
multitask setting of our design, both the token
classification and sequence classification tasks
benefit from BERT’s strength. Además, nosotros
notice that when doing the single turn setting,
the system response usually depends on certain
information mentioned in the former turn user
utterance. Por lo tanto, we concatenate the former
turn utterance to each current single turn as the
input for BERT. Under this setting, we achieved
in performance regarding joint
a large boost
accuracy as in Table 3. It provides an excellent
base for the later stage inferences.
We also tested the effect of reasoning over
the database. For a clear comparison, we ignore
the evidence obtained via bipartite graph belief
propagation while keeping other settings the same.
To show it more clearly, we re-organize the
results in Table 4. It can be observed that both
ReDST M LP and ReDST GRU gain a bit from
belief propagation. It validates the usefulness of
database reasoning. Sin embargo, since the graph
is rather small, the performance improvement is
rather limited. Similar patterns are found in Chen
et al., (2020) and we suspect that it will be more
helpful with larger database structure. También, nosotros
will further explore its usage in down-stream tasks
such as action prediction.
For different incremental reasoning modules,
the results are also shown in Table 1. We find
that ReDST GRU performs better. Sin embargo, nosotros
notice that simply accumulating turn belief as in
Zhong et al. (2018) performs very well. The rule
is to add newly predicted turn belief entries to the
last joint belief. When different values for a slot
appear, only keep the new one. Although this rule
seems simple, it actually reflects the dialogue’s
interactive and updating nature. We tried to
directly apply this rule on the ground truth turn
belief to generate joint belief. It results in 0.963
joint accuracy. Sin embargo, a critical problem of
such accumulation rule is that when the generated
turn belief is wrong, it will not be able to add a
missing entry or delete a wrong entry. By applying
GRU in ReDST GRU , it manages to modify a bit
with the help of database evidence. Still, hay
large space for more powerful reasoning models
to address this error accumulation issue. Lo haremos
further investigate in this direction.
4.5 Análisis de errores
We also provide error analysis regarding each slot
for ReDST GRU in Figure 3. To make it more clear,
we also list the results of SOM for comparison. Nosotros
observe that a large portion of the improvements
for our method are on name entities and time-
related slots. As mentioned in Wu et al. (2019),
name slots in the attraction, restaurant, y
hotel domains have the highest error rates. Él
is partly because these slots have a relatively
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algorithms for performing reasoning over turns
and on graphs for generating more accurate
summarization of user intention.
Expresiones de gratitud
This research is supported by the National Re-
search Foundation, Singapur, under its Inter-
national Research Centres in Singapore Funding
Initiative. Any opinions, findings, and conclusions
or recommendations expressed in this material are
those of the author(s) and do not reflect the views
of National Research Foundation, Singapur.
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