# MoNET: Tackle State Momentum via Noise-Enhanced Training
for Dialogue State Tracking

Haoning Zhang1,3, Junwei Bao2 , Haipeng Sun2,
Youzheng Wu2, Wenye Li4,5, Shuguang Cui3,1,6, Xiaodong He2
1FNii, CUHK-Shenzhen 2JD AI Research
3SSE, CUHK-Shenzhen 4SDS, CUHK-Shenzhen 5SRIBD 6Pengcheng Lab
<EMAIL_ADDRESS>{wyli<EMAIL_ADDRESS>
{baojunwei, sunhaipeng6, wuyouzheng1<EMAIL_ADDRESS>Corresponding
author<EMAIL_ADDRESS>
###### Abstract

Dialogue state tracking (DST) aims to convert the dialogue history into
dialogue states which consist of slot-value pairs. As condensed structural
information memorizes all dialogue history, the dialogue state in the previous
turn is typically adopted as the input for predicting the current state by DST
models. However, these models tend to keep the predicted slot values
unchanged, which is defined as state momentum in this paper. Specifically, the
models struggle to update slot values that need to be changed and correct
wrongly predicted slot values in the previous turn. To this end, we propose
MoNET to tackle state momentum via noise-enhanced training. First, the
previous state of each turn in the training data is noised via replacing some
of its slot values. Then, the noised previous state is used as the input to
learn to predict the current state, improving the model’s ability to update
and correct slot values. Furthermore, a contrastive context matching framework
is designed to narrow the representation distance between a state and its
corresponding noised variant, which reduces the impact of noised state and
makes the model better understand the dialogue history. Experimental results
on MultiWOZ datasets show that MoNET outperforms previous DST methods.
Ablations and analysis verify the effectiveness of MoNET in alleviating state
momentum issues and improving the anti-noise ability111Our code is available
at https://github.com/JD-AI-Research-NLP/MoNET.

Figure 1: A dialogue example of three turns, containing the system utterance
(U), the user response (R), the ground truth dialogue state (GT), and the
prediction of each turn (Pred). The state “hotel-book day-Saturday” is
predicted in the first turn (marked in blue). The dotted arrow represents the
ideal predictions, i.e., update slot values that need to be changed (Turn 2)
and correct wrongly predicted slot values in the previous turn (Turn 3). The
solid arrow represents the predictions (marked in red) with state momentum
issues.

##  1 Introduction

Dialogue state tracking (DST) is a core component in modular task-oriented
dialogue systems Hosseini-Asl et al. (2020); Yang et al. (2021); Sun et al.
(2022, 2023). It extracts users’ intents from the dialogue history and
converts them into structural dialogue states, i.e., sets of slot-value pairs.
An accurate dialogue state is crucial for generating correct dialogue action
and suitable natural language responses, which are the main tasks of dialogue
management and natural language generation components (Williams and Young,
2007; Thomson and Young, 2010; Young et al., 2010). Earlier DST approaches
predict the state directly from the dialogue history (natural language
utterances) (Mrkšić et al., 2017; Xu and Hu, 2018; Wu et al., 2019; Chen et
al., 2020a). Since the dialogue state is condensed structural information
memorizing all dialogue history, recent methods incorporate the previously
predicted state as the input besides the dialogue history (Ouyang et al.,
2020; Kim et al., 2020; Ye et al., 2021).

Conventional DST models taking the previous state as the input usually show
the characteristic that the previously predicted slot values tend to be kept
unchanged when predicting the current state, defined as state momentum in this
paper. The state momentum makes DST models struggle to modify the previous
prediction, which affects the performance when the values of some slots need
to be updated as the user’s intent changes, and there exist wrongly predicted
slot values that need to be corrected. Figure 1 gives an example of a dialogue
involving three turns with two types of state momentum issues. The state
hotel-book day-Saturday is predicted in Turn 1 and keeps unchanged in the next
two turns, while the user’s request is updated into Sunday in Turn 2.
Consequently, the predicted state becomes wrong in the following two turns.
The dotted arrow represents the ideal prediction cases: the value is updated
with the ground truth changes and is corrected when becoming a wrong input.
The solid arrow represents the state momentum issues, where the state is kept
unchanged, leading to two consecutive wrong predictions. One possible reason
for the state momentum issue is that in the training data, most slot values in
the previous turn are the same as those in the current turn, which limits the
ability of conventional DST models to modify slot values during inference. To
address this limitation, an intuitive idea is to augment training instances
with a higher ratio of slots whose previous values differ from those in the
current turn. By incorporating such examples, the DST model can learn to deal
with more cases where modifying previous predictions is required. Besides, if
the DST model can treat wrong and correct dialogue states similarly in
representations, then the former will typically help make further predictions.
In other words, by treating incorrect dialogue states as valuable information,
the DST model can potentially identify and correct erroneous slot values.

In this paper, we propose MoNET to tackle the state momentum issue via a
noise-enhanced training strategy. The core idea is to manually add noise into
the previous state to simulate scenarios with wrong state input. First, the
previous state of each turn in the training data is noised via replacing some
of its slot values. Specifically, for each active slot (with a non-$none$
value), we replace its value with a certain probability. Then, the noised
previous state, concatenated with the dialogue history, is used as the input
to learn to predict the current state, improving the model’s ability to update
and correct slot values. Furthermore, a contrastive context matching framework
is designed to narrow the representation distance between a state and its
corresponding noised variant, which reduces the impact of the noised state and
makes the DST model better understand the dialogue history. Such approaches
make the model less sensitive to the noise, and enhance its ability to modify
the slot values of previous states in current predictions. Experiments on the
multi-domain dialogue datasets MultiWOZ 2.0, 2.1, and 2.4 show that our MoNET
outperforms previous DST models. Ablation studies and analysis further verify
the effectiveness of the proposed noised DST training and the contrastive
context matching framework in alleviating state momentum and improving the
model’s anti-noise ability.

The contributions are summarized as follows: (1) We define the state momentum
issue in DST, where models tend to keep the predicted slot values unchanged,
namely, struggling to update and correct them from the previous turn. (2) We
propose MoNET to tackle the state momentum issue via noised DST training and
the contrastive context matching framework. (3) We conduct comprehensive
experiments on three datasets, MultiWOZ 2.0, 2.1, and 2.4. The results
demonstrate that MoNET outperforms previous DST methods, showcasing its
effectiveness in alleviating the state momentum issue.

##  2 Related Work

###  2.1 Dialogue State Tracking

Traditional DST approaches focus on single-domain dialogue state tracking
(Williams and Young, 2007; Thomson and Young, 2010; Lee and Kim, 2016). Recent
researches pay more attention to multi-domain DST using distributed
representation learning (Wen et al., 2017; Mrkšić et al., 2017). Previous
works implement Seq2seq frameworks to encode the dialogue history, then
predict the dialogue state from scratch at every turn (Rastogi et al., 2017;
Ren et al., 2018; Lee et al., 2019; Wu et al., 2019; Chen et al., 2020a).
Utilizing dialogue history is limited for larger turns, since the state of
each turn is accumulated from all previous turns, while it’s hard to retrieve
state information from a long history.

Current works mainly incorporate the previous state as the model input, which
is regarded as an explicit fixed-sized memory (Ouyang et al., 2020; Ye et al.,
2022a; Wang et al., 2022). Kim et al. (2020) propose a state operation sub-
task, where the model is trained to first predict the operation of each slot-
value pair, such as UPDATE, CARRYOVER, etc., then only the value of a minimal
subset of slots will be newly modified (Zeng and Nie, 2020; Zhu et al., 2020).
These methods enhance model prediction efficiency and the ability to update
slot-value pairs. Tian et al. (2021) deal with the error propagation problem
that mistakes are prone to be carried over to the next turn, and design a two-
pass generation process, where a temporary state is first predicted then used
to predict the final state, enhancing the ability to correct wrong
predictions. In this paper, we use “state momentum” to define the issue where
the wrong dialogue state is predicted due to that the previous prediction
keeps unchanged, either it should be updated or corrected. To the best of our
knowledge, this is the first time to systematically tackle the issue caused by
continuous unchanged predictions in the multi-turn DST task.

###  2.2 Contrastive Learning

Contrastive learning aims to generate high-quality representations by
constructing pairs of similar examples to learning semantic similarity (Mnih
and Teh, 2012; Baltescu and Blunsom, 2015; Peters et al., 2018). The goal is
to help the model semantically group similar instances together and separate
dissimilar instances. During training, the neighbors with similar semantic
representations (positive pairs) will be gathered, while the non-neighbors
(negative pairs) will be pushed apart, enabling the learning of more
meaningful representations. In the NLP area, semantic representations can be
learned through self-supervised methods, such as center word prediction in
Word2Vec, next sentence prediction in BERT, sentence permutation in BART, etc
(Mikolov et al., 2013; Devlin et al., 2019; Lewis et al., 2020). Recent
approaches build augmented data samples through token shuffling, word
deletion, dropout, and other operations (Cai et al., 2020; Klein and Nabi,
2020; Yan et al., 2021; Wang et al., 2021; Gao et al., 2021; Zhang et al.,
2022). In this paper, we construct augmented samples based on the noised and
original dialogue state. Given context inputs with the same dialogue history
and different states, the model is trained to gather them into similar
objects, aiming to learn better representations, reduce the impact of noise,
and better understand the dialogue history.

##  3 Methodology

Figure 2: The model description and noised input construction example. The
left part (a) shows the architecture of the MoNET model. A context input
representation $H^{n}_{t}$ in an $N$-size batch is shown in the contrastive
context matching framework as an example, where $H^{n+}_{t}$ is the context
representation of its noised variant. The right part (b) gives an example of
constructing noised context input. For each active slot in state $B_{t-1}$,
given a noise threshold $p$, a random number $a$ is selected. If $a<p$, then
its value is replaced with another one randomly selected from the ontology
(suppose the pair “train-day-Saturday” is replaced with “train-day-Friday”);
otherwise the value will be kept unchanged (suppose the pairs “train-
departure-Birmingham” and “train-destination-Cambridge” are unchanged).

###  3.1 Problem Formulation

In this paper, we focus on building a dialogue state tracking (DST) model
which accurately predicts the dialogue state based on the dialogue history and
the previous state during multi-turn dialogue interactions. A dialogue state
consists of domain-slot-value tuples, typically corresponding to the dialogue
topic, the user’s goal, and the user’s intent. Following previous studies, in
the rest of this paper, we omit “domain” and use “slot” to refer to a “domain-
slot” pair. All slot-value pairs are from a pre-defined ontology.

Formally, let’s define $D_{t}=[U_{t},R_{t}]$ as a pair of system utterance
$U_{t}$ and user response $R_{t}$ in the $t$-th turn of a multi-turn dialogue,
and $B_{t}$ as the corresponding dialogue state. Each state $B_{t}$ contains a
set of slot-value pairs, i.e.,
$B_{t}=\\{(S_{j},V^{i}_{j})|j\\!\in\\![1\\!:\\!J]\\}$, where $J$ is the total
number of slots, and $V^{i}_{j}\in\mathcal{V}_{j}$ is one of the values in
$\mathcal{V}_{j}$ for the $j$-th slot $S_{j}$ in the ontology. Given the
dialogue history $\\{D_{1},...,D_{t}\\}$ and previous state $B_{t-1}$, the
goal of the DST task is to predict the current dialogue state $B_{t}$.

###  3.2 MoNET

As introduced in Section 1, solving the state momentum issue is crucial to the
DST task. Therefore, in this paper, we propose MoNET to tackle the state
momentum issue via a noise-enhanced training strategy to enhance the model’s
ability to update and correct slot values. The architecture of MoNET is shown
in Figure 2(a), which consists of context BERT encoders, slot and value BERT
encoders, the slot-context attention module, the slot-value matching module,
and the contrastive context matching framework. Each of them will be
elaborated on in this section.

####  3.2.1 Base Architecture

We first introduce the base architecture of our MoNET, similar to the backbone
model in (Ye et al., 2022a). A model trained only with the base architecture
of MoNET is noted as “Baseline”, and evaluated in Section 5 to compare the
difference in performance with the whole MoNET model.

##### Context Encoder.

A BERT encoder encodes the context input, which is the concatenation of the
dialogue history and the state in the previous turn:

$\begin{aligned} &X_{t}=f(M_{t},B_{t-1},D_{t})\\\ &=[CLS]\oplus M_{t}\oplus
B_{t-1}\oplus[SEP]\oplus D_{t}\oplus[SEP],\end{aligned}$

(1)

where $M_{t}=D_{1}\oplus,...,\oplus D_{t-1}$ contains previous utterances,
$B_{t-1}$ is the state containing the active slots in the previous turn,
$[CLS]$ and $[SEP]$ are special tokens of the BERT encoder. Then the
representations of the context input are derived:

$H_{t}=BERT(X_{t})\in\mathbb{R}^{|X_{t}|\times d},$ (2)

where $|X_{t}|$ is the total number of tokens in $X_{t}$, and $d$ is the
encoded hidden size.

##### Slot and Value Encoders.

The BERT encoders with fixed parameters are used to derive the slot and value
representations:

$\displaystyle h_{S_{j}}=BERT_{fixed}(S_{j})_{[CLS]},$ (3) $\displaystyle
h_{V^{i}_{j}}=BERT_{fixed}(V^{i}_{j})_{[CLS]},$

where states $h_{S_{j}},h_{V^{i}_{j}}\in\mathbb{R}^{1\times d}$ are the
$[CLS]$ representations of the slot and value.

##### Slot-Context Attention.

For each slot $S_{j}$, its slot-context-specific feature is extracted by the
multi-head attention mechanism (Vaswani et al., 2017):

$r^{t}_{S_{j}}=LN(MultiHead(h_{S_{j}},H_{t},H_{t}))\in\mathbb{R}^{1\times d},$
(4)

where $LN$ is the normalization layer.

##### Slot-Value Matching.

The probability of predicting the value $V^{i}_{j}$ of the slot $S_{j}$ is
derived by calculating the $L2$-distance between the value representation
$h_{V^{i}_{j}}$ and the slot-context representation $r^{t}_{S_{j}}$, which is
denoted as:

$P_{\theta}(V^{i}_{j}|X_{t},S_{j})=\frac{exp(-||r^{t}_{S_{j}}-h_{V^{i}_{j}}||_{2})}{\sum_{k\in[1:|\mathcal{V}_{j}|]}exp(-||r^{t}_{S_{j}}-h_{V^{k}_{j}}||_{2})}$,

(5)

where $\theta$ are trainable parameters of the model.

##### Training and Inference.

During training, the ground dialogue state is used to form the context input
$X_{t}$ (teacher-forcing). For the $t$-th turn, the loss is the sum of the
negative log-likelihood among all $J$ slots as follows:

$L_{d_{ori}}=\sum_{j=1}^{J}-log(P_{\theta}(V^{i^{*}}_{j}|X_{t},S_{j})),$ (6)

where $V^{i^{*}}_{j}$ is the ground truth value of the slot $S_{j}$ at turn
$t$. During inference, the previously predicted state is used to form the
context input $X_{t}$, and the value of the slot $S_{j}$ is predicted by
selecting the one with the smallest distance, corresponding to the largest
probability:

${V^{\widehat{i}}_{j}}=\mathop{argmax}_{i\in[1:|\mathcal{V}_{j}|]}P_{\theta}(V^{i}_{j}|X_{t},S_{j}).$
(7)

####  3.2.2 Noised Data Construction

As described previously, an intuitive idea to tackle the state momentum issue
is to increase the number of training instances where the slot-value pairs in
the previous turn are different from those in the current turn. Based on this
point, we attempt to utilize noised data to train the DST model.

Generally, for each active slot (with a non-$none$ value) in the previous
dialogue state, we involve noise by replacing its original value with another
value with a probability $p$ (used as the noise threshold), e.g., as the
example shown in Figure 2(b). Formally, at each training step, given a batch
of training instances, a noised context input $X^{+}_{t}$ is constructed for
each instance based on its original context input
$X_{t}=f(M_{t},B_{t-1},D_{t})$ as follows:

$\displaystyle X^{+}_{t}=$ $\displaystyle f(M_{t},B^{+}_{t-1},D_{t}),$ (8)
$\displaystyle B^{+}_{t-1}=$
$\displaystyle\\{(S_{j},V^{i^{+}}_{j})|j\\!\in\\![1\\!:\\!J]\\}.$

For each active slot $S_{j}$ in $B_{t-1}=\\{(S_{j},V^{i}_{j})\\}$, a real
number $a\in[0,1]$ is sampled to determine whether the original $V^{i}_{j}$ is
replaced with a randomly selected value
$V^{k}_{j}\in\mathcal{V}_{j}\setminus\\{V_{j}^{i}\\}$ from the ontology or
kept unchanged:

$\displaystyle V^{i^{+}}_{j}=\left\\{\begin{array}[]{ll}V^{k}_{j},&if\quad
a<p\\\ V^{i}_{j},&if\quad a\geq p.\end{array}\right.$ (9)

####  3.2.3 Noised State Tracking

Similar to $X_{t}$, the noised context instance $X_{t}^{+}$ is also used as
the model input to predict the state $B_{t}$ as the training target, aiming to
improve the model’s ability to dynamically modify the previous slot values in
current predictions. Specifically, the representation $H_{t}^{+}$ of
$X^{+}_{t}$ is first derived by the BERT context encoder mentioned in Section
3.2.1:

$H_{t}^{+}=BERT(X^{+}_{t})\in\mathbb{R}^{|X^{+}_{t}|\times d}.$ (10)

Then, similar to the previous process, for each slot $S_{j}$, $X^{+}_{t}$ is
used to predict its value based on the distribution
$P_{\theta}(V^{i}_{j}|X^{+}_{t},S_{j})$. Eventually, the loss for the noised
state tracking can be denoted as:

$L_{d_{nos}}=\sum_{j=1}^{J}-log(P_{\theta}(V^{i^{*}}_{j}|X^{+}_{t},S_{j})).$
(11)

####  3.2.4 Contrastive Context Matching

Inspired by contrastive learning approaches which group similar samples closer
and diverse samples far from each other, a contrastive context matching
framework is designed to narrow the representation distance between $X_{t}$
and its noised variant $X_{t}^{+}$, aiming to reduce the impact of the noised
state $B_{t-1}^{+}$ and help the model better understand the dialogue history.
Specifically, in a batch of $N$ instances with the original context input
$\mathbf{X}_{t}=\\{X_{t}^{n}\\}_{n=1}^{N}$, we construct $N$ corresponding
noised instances with the context input
$\mathbf{X}_{t}^{+}=\\{X_{t}^{n+}\\}_{n=1}^{N}$. To clearly describe the
context inputs, in this section, we temporarily involve $n$ into $X_{t}$ &
$H_{t}$ as $X_{t}^{n}$ & $H_{t}^{n}$ to indicate the in-batch index.

For each context input $X_{t}^{n}$, its noised sample $X_{t}^{n+}$ is regarded
as its positive pair, and the rest $(2N\\!-\\!2)$ instances in the same batch
with different dialogue histories are considered negative pairs. Then the
model is trained to narrow the distance of the positive pair and enlarge the
distance of negative pairs in the representation space with the following
training objective:

$L_{c}=-log(\frac{exp(sim(H^{n[cls]}_{t},H_{t}^{n+[cls]})/\tau)}{\sum_{k=1}^{2N}\mathbbm{1}_{[k\neq
n]}exp(sim(H_{t}^{n[cls]},H_{t}^{k[cls]})/\tau)})$,

(12)

where $H_{t}^{n[cls]}$ and $H_{t}^{n+[cls]}$ are the $[CLS]$ representations
of $H_{t}^{n}$ and $H_{t}^{n+}$, $\tau$ is the temperature parameter, and
$sim(\cdot)$ indicates the cosine similarity function (Chen et al., 2020c).

####  3.2.5 Optimization

The total training loss for each instance is the sum of losses from the slot-
value matching for DST and the contrastive context matching for representation
learning, where the former is the average of the losses using the original or
the noised context input mentioned in Section 3.2.1 and 3.2.3:

$L_{tot}=(L_{d_{ori}}+L_{d_{nos}})/2+L_{c}.$ (13)

Baseline | Pre-trained Model | MultiWOZ 2.0 | MultiWOZ 2.1 | MultiWOZ 2.4
---|---|---|---|---
Joint | Slot | Joint | Slot | Joint | Slot
TRADE (Wu et al., 2019) | - | 48.62 | 96.92 | 45.60 | 96.55 | 55.05 | 97.62
SUMBT (Lee et al., 2019) | BERT-base | 42.40 | - | 49.01 | 96.76 | 61.86 | 97.90
PIN (Chen et al., 2020a) | - | 52.44 | 97.28 | 48.40 | 97.02 | 58.92 | 98.02
SOM-DST (Kim et al., 2020) | BERT-base | 51.72 | - | 53.01 | - | 66.78 | 98.38
CSFN-DST (Zhu et al., 2020) | BERT-base | 52.23 | - | 53.19 | - | - | -
DST-Picklist (Zhang et al., 2020) | BERT-base | 54.39 | - | 53.30 | 97.40 | - | -
SAVN (Wang et al., 2020) | BERT-base | 54.52 | 97.42 | 54.86 | 97.55 | 60.55 | 98.05
SST (Chen et al., 2020b) | BERT-base | 51.17 | - | 55.23 | - | - | -
SimpleTOD (Hosseini-Asl et al., 2020) | DistilGPT2 | - | - | 55.26 | - | - | -
Seq2SeqDU (Feng et al., 2021) | BERT-base | - | - | 56.10 | - | - | -
STAR (Ye et al., 2021) | BERT-base | 54.53 | - | 56.36 | 97.59 | 73.62 | 98.85
SDP-DST (Lee et al., 2021) | T5-base | - | - | 56.66 | - | - | -
DS-Graph (Lin et al., 2021) | GPT2 | 54.86 | 97.47 | - | - | - | -
DSGFNet (Feng et al., 2022) | BERT-base | - | - | 56.70 | - | - | -
PPTOD (Su et al., 2022) | T5-large | 53.89 | - | 57.45 | - | - | -
Baseline | BERT-base | 54.38 | 97.47 | 55.82 | 97.51 | 73.81 | 98.82
MoNET | BERT-base |  55.48 ($\uparrow$ 1.10) | 97.55 |  57.71 ($\uparrow$ 1.89) | 97.71 |  76.02 ($\uparrow$ 2.21) | 98.99
Use Modified Label |  |  |  |  |  |  |
TripPy (Heck et al., 2020) | BERT-base | - | - | 55.29 | - | - | -
TripPy + SCoRe (Yu et al., 2021) | BERT-base | - | - | 60.48 | - | - | -
TripPy + CoCoAug (Li et al., 2021) | BERT-base | - | - | 60.53 | - | - | -
TripPy + SaCLog (Dai et al., 2021) | BERT-base | - | - | 60.61 | - | - | -

Table 1: Joint and slot goal accuracy of our MoNET and several previous
methods on three MultiWOZ test sets.

##  4 Experiment Setting

###  4.1 Datasets

We choose MultiWOZ, 2.0, 2.1, and 2.4 versions as our datasets. MultiWOZ 2.0
(Budzianowski et al., 2018) is a standard human-human conversational dialogue
corpus with seven domains. MultiWOZ 2.1 (Eric et al., 2020) has the same
dialogues as the 2.0 version, where some incorrect state labels are re-
annotated. Both of them are widely used in previous DST approaches. MultiWOZ
2.4 (Ye et al., 2022b) is the latest refined version correcting all the
incorrect state labels in validation and test sets. All three datasets contain
the same number of dialogues, which are 8438/1000/1000 in
train/validation/test sets. For the three datasets, we follow the previous
work (Wu et al., 2019) to use five domains (attraction, hotel, restaurant,
taxi, train) with 30 domain-slot pairs in experiments, since the dialogues in
the remaining domains are not in the validation and test sets.

###  4.2 Evaluation Metrics

We use joint and slot goal accuracy as the evaluation metrics. Joint goal
accuracy is the ratio of dialogue turns where the values of all slots are
correctly predicted. Slot goal accuracy is the ratio of domain-slot pairs
whose values are correctly predicted. Both of them include correctly
predicting those inactive slots with the value $none$.

###  4.3 Existing Methods

We compare the performance of our MoNET with several existing methods, i.e.,
TRADE, SUMBT, PIN, SOM-DST, CSFN-DST, DST-Picklist, SAVN, SST, SimpleTOD,
TripPy, STAR, SDP-DST, DS-Graph, DSGFNet, PPTOD shown in Table 1, and our base
architecture mentioned in Section 3.2.1, denoted as Baseline.

###  4.4 Training Details

The BERT-base-uncased model is used as the context, slot and value encoders,
with 12 attention layers and a hidden size of 768. During training, only the
parameters of the context BERT encoder are updated, while the parameters of
the slot and value BERT encoders are not. The batch size is set to 8. The
AdamW optimizer is applied to optimize the model with the learning rate 4e-5
and 1e-4 for the context encoders and the remaining modules, respectively
(Loshchilov and Hutter, 2019). The temperature parameter $\tau$ is set to 0.1.
The noise threshold $p$ defined in Section 3.2.2 is set to 0.3, and its impact
on model performance is discussed in Section 5. All models are trained on a
P40 GPU device for 6-8 hours.

##  5 Results and Analysis

###  5.1 Main Results

Table 1 shows performances of MoNET and baselines on MultiWOZ 2.0, 2.1 and
2.4. Among them, TripPy and its modified versions employ a ground truth label
map of synonyms replacement as extra supervision, which increases their
accuracy scores and differs from other methods of testing with common labels.
As can be observed, MoNET achieves the joint goal accuracy scores of 55.48%,
57.71%, 76.02% in three datasets, which are impressive results compared with
previous methods, and has improvements of 1.10%, 1.89%, and 2.21% on the
Baseline model, indicating that our proposed noise-enhanced training helps the
model make better predictions.

Besides the general joint and slot goal accuracy, we also calculate the slot-
level proportion of state momentum errors over all wrong predictions. We train
the Baseline model and make predictions on the MultiWOZ 2.4 test set. For each
dialogue, starting from the second turn, we add up each wrong predicted slot-
value pair which also exists in the previous turn. Finally, there are 844 such
wrong slot-value pairs, and the number of all the wrong predicted pairs is
2603, hence the proportion is (844/2603)*100%=32.4%, and our MoNET model
modifies 47.0% of them (397 in 844 are correctly predicted). Moreover, in
MultiWOZ 2.4 training set annotations, for each dialogue turn (also except the
first turn of each dialogue), around 78.1% slot-value pairs exist in the
previous turn, since the slot-value pairs will be accumulated as the dialogue
progresses. The results further indicate the issue caused by those unchanged
slot-value pairs during multi-turn interactions, and the effectiveness of our
method in enhancing the model’s ability to modify previous predictions.

Model | NoisedCM | NoisedST | Accuracy
---|---|---|---
Baseline w/o state | $\times$ | $\times$ | 64.94
Baseline | $\times$ | $\times$ | 73.81
MoNET-ST | $\times$ | ✓ | 75.54
MoNET-CM | ✓ | $\times$ | 75.76
MoNET | ✓ | ✓ | 76.02

Table 2: Joint goal accuracy on the MultiWOZ 2.4 test set of MoNET and four
ablated modifications.

###  5.2 Ablation Study

To explore the individual contribution of each part of our model, we compare
the whole MoNET with several ablated versions. First, we remove the previous
dialogue state from the context input of the Baseline model, where the
modified context input is $X_{t}=[CLS]\oplus M_{t}\oplus[SEP]\oplus
D_{t}\oplus[SEP]$, denoted as Baseline w/o state; besides, the two noise-
enhanced methods are removed from the MoNET respectively, denoted as MoNET-CM
(context matching only) and MoNET-ST (noised state tracking only).

Table 2 shows the joint goal accuracy performances of the full MoNET model and
its four modifications on the MultiWOZ 2.4 test set. As can be observed,
Baseline w/o state gets the lowest accuracy, demonstrating that explicitly
using the previous dialogue state as part of the model input is beneficial to
make predictions, even though there may exist wrong slot-value pairs. Besides,
both MoNET-CM and MoNET-ST outperform the Baseline model, demonstrating
functionalities of the noised state tracking in modifying slot-value pairs in
further turns, and the context matching framework in learning improved
semantic representations. Moreover, MoNET derives the best performance,
demonstrating the effectiveness of integrating the two parts into a unified
noised-enhance training strategy.

Figure 3: Turn-level joint goal accuracy and accuracy difference between MoNET
and Baseline on the MultiWOZ 2.4 test set.

###  5.3 Turn-Level Evaluation

Figure 3 shows the turn-level joint goal accuracy of MoNET and Baseline
models, as well as the percentage difference in accuracy (the difference
between the two models’ accuracy divided by the accuracy of Baseline) on the
MultiWOZ 2.4 test set. Generally, the state momentum issue becomes more
apparent in dialogues with larger turns, since they always contain more active
slot-value pairs, and any one of the wrong pairs kept unchanged will affect
the further prediction accuracy. With the increase of turns, the accuracy of
Baseline harshly degrades, while MoNET gets a relatively smaller decline,
resulting in a gradually increasing and evident percentage difference in
accuracy. This demonstrates the superiority of MoNET in alleviating the
accuracy decrease caused by the state momentum issue, especially in those
dialogues with larger than 6-7 turns.

Figure 4: Performance on the MultiWOZ 2.4 validation set w.r.t the noise
threshold of adding noise. Figure 5: Performances on the MultiWOZ 2.4 test set
w.r.t. the noise threshold (corresponding to the noised slot-value pair ratio
in the dialogue state input). The left one is the joint goal accuracy, and the
right one is the active slot-context features similarity.

###  5.4 Noise Threshold Selection for Training

To explore the impact of different probabilities of adding noise into the
context input for training, we vary the noise threshold $p$ from 0 to 0.5 to
train our MoNET. The results on the MultiWOZ 2.4 validation set are shown in
Figure 4, where MoNET achieves the best performance when the noise threshold
$p$ is set to 0.3. Intuitively, a small $p$ makes the noised context input
contain fewer noised slot-value pairs (hard to learn meaningful semantics from
the noised data); conversely, a large $p$ makes the noised context input far
from the original context input in the representation space (hard to group
them closer). Both two cases make the model hard to learn effective features
from the noised context input, leading to lower prediction accuracy. Hence,
the empirical probability of adding noise is important to derive the best
performance of the DST model.

Figure 6: Attention visualizations of the two dialogue examples mentioned in Table 3. | Sys: I have many trains available. What day would you like to leave?
---
Usr: We will be leaving on Sunday afternoon.
GT: train-day-Sunday
Baseline: train-day-Sunday MoNET: train-day-Sunday
| Sys: There are still many trains to pick from, can you narrow down a
---
departure and arrival time frame?
Usr: Yes, it should leave after 20:15 and leave on Monday, not Sunday.
Please give me a price, travel time, and arrival time for any train.
GT: train-day-Monday
Baseline: train-day-Sunday MoNET: train-day-Monday (update)
| Sys: Great! You are booked at Autumn House for 1 night. Your reference
---
number is n4tvfkgs. Would you like more information?
Usr: I would also like a taxi between the places if possible.
GT: taxi-destination-Autumn House
Baseline: taxi-destination-Gonville and Caius College
MoNET: taxi-destination-Gonville and Caius College
| Sys: I can help with that. When would you like to either leave or arrive?
---
And do you want the taxi from the college to the hotel or the other
way around?
Usr: I need to go from the college to the hotel, and I want to leave the
college by 20:45, please.
GT: taxi-destination-Autumn House
Baseline: taxi-destination-Gonville and Caius College
MoNET: taxi-destination-Autumn House (correct)
Table 3: Predictions of two dialogue examples on MultiWOZ 2.4 separated by the
double solid line, corresponding to two state momentum cases. Wrong and
correct predicted values are marked in red and blue.

###  5.5 Anti-noise Probing with Noise Testing

In this section, we conduct noise testing to explore the impact of anti-noise
ability on DST models. We first evaluate DST performances of MoNET and
Baseline by introducing different ratios of noise (with $p$ from 0 to 1) into
the oracle previous dialogue state as the model input. Figure 5 shows the
performances of MoNET and Baseline on MultiWOZ 2.4. Both of them get high
accuracy when the noise ratio is 0, as we use the oracle previous dialogue
state as the model input; with the increase of the noise ratio, the joint goal
accuracy of Baseline gets a sharp decline, while MoNET degrades much more
smoothly. Furthermore, for each dialogue turn, we also show the $L2$-distance
between the original and noised context representations, i.e., the mean
pooling of all token representations $H_{t}$ and $H_{t}^{+}$. As can be
observed, along with the increase of noise ratio, the distance between the two
representations of MoNET is much lower than that of Baseline. These results
indicate that MoNET achieves a higher anti-noise ability by generating
relatively similar representations for the original and noised contexts, which
helps the DST model maintain an acceptable performance even with a high ratio
of noise in its input.

###  5.6 Case Study and Attention Visualization

Table 3 gives two prediction examples using MoNET and Baseline on the MultiWOZ
2.4 test set, corresponding to the two types of state momentum cases. In the
first one, they correctly predict the slot-value pair “train-day-Sunday”,
while only MoNET updates it in the next turn along with the ground truth
changing into “train-day-Monday”. In the second one, they make a wrong
prediction “taxi-destination-Gonville and Caius College”. While Baseline keeps
it unchanged in the next turn, MoNET corrects it, resulting in a joint goal
accuracy of 100% for the second turn. Besides, we further explore these two
examples by calculating and visualizing the overall attention scores, which
are shown in Figure 6. For each slot, its overall attention score over each
token is the weighted sum of the self-attended scores by all tokens in
$X_{t}$. The weights come from the slot-context attention, and the self-
attended scores are the average of attention scores over multiple layers in
BERT. As can be observed, Baseline pays more attention to the values in the
previously predicted state, and fails to solve the state momentum issues;
MoNET pays relatively higher attention to the correct tokens (“monday” in the
first case and “autumn house” in the second case), and consequently,
successfully updates Sunday into Monday and corrects Gonville and Caius
College into Autumn House. These examples and attention visualizations
indicate the effectiveness of our MoNET in alleviating the two types of state
momentum issues.

Model | Accuracy
---|---
MinTL Lin et al. (2020) | 52.07
MTTOD Lee (2021) | 53.56
PPTOD Su et al. (2022) | 53.37
T5-base | 53.26
MoNET (T5-base) | 54.67 ($\uparrow$ 1.41)
Table 4: Joint goal accuracy on MultiWOZ 2.0 test set of baselines using the
same T5-base pre-trained model.

###  5.7 Extension on Generation-based Models

In addition to the original classification-based MoNET model, we also evaluate
our approach using a simple generation framework using T5-base as the backbone
pre-trained model Raffel et al. (2020). The ontology is built from the
database and training set annotations, which is only used for noise value
construction. The model framework is similar to the BERT-based MoNET in Figure
2(a), where the BERT encoders and slot-value matching modules are replaced
with T5 encoders and decoders. The T5 encoders encode the dialogue context
inputs, slots, and values. After deriving the slot-context attentive
representations, the T5 decoders generate each slot-value pair. Table 4 shows
the joint goal accuracy performance of the T5-based MoNET on the MultiWOZ 2.0
test set, compared with other end-to-end/generation-based models using the
same T5-base pre-trainied model. As can be observed, our modified MoNET
outperforms the T5-base backbone and others with the same T5-base model,
indicating its effectiveness and adaptability for the implementation of
generation-based methods.

##  6 Conclusion

In this paper, we define and systematically analyze the state momentum issues
in the DST task, and propose MoNET, a training strategy equipped with noised
DST training and the contrastive context matching framework. Extensive
experiments on MultiWOZ 2.0, 2.1, and 2.4 datasets verify its effectiveness
compared with existing DST methods. Supplementary studies and analysis
demonstrate that MoNET has a stronger anti-noise ability which helps alleviate
the state momentum issues.

## Limitations

Our proposed MoNET is a classification-based method requiring the pre-defined
ontology containing all slot-value pairs. Moreover, during prediction, for
each slot, its distance with all possible values is calculated, i.e., the
prediction has to process 30 times, which is the number of slots in the
MultiWOZ dataset. Compared with the generation methods that only process once
and do not need ontology, our method is short in training efficiency and
scalability. However, most task-oriented dialogue datasets contain their
knowledge base containing slot value information, so it’s acceptable to
construct the ontology for random sampling. Besides, the results in Section
5.7 demonstrate that our method can be implemented into generation-based
backbone models.

## Acknowledgements

The work was supported in part by NSFC with Grant No. 62293482, the Basic
Research Project No. HZQB-KCZYZ-2021067 of Hetao Shenzhen-HK S&T Cooperation
Zone, the National Key R&D Program of China with grant No. 2018YFB1800800, the
Shenzhen Outstanding Talents Training Fund 202002, the Guangdong Research
Projects No. 2017ZT07X152, No. 2019CX01X104, and No. 2021A1515011825, the
Guangdong Provincial Key Laboratory of Future Networks of Intelligence (Grant
No. 2022B1212010001), the Shenzhen Key Laboratory of Big Data and Artificial
Intelligence (Grant No. ZDSYS201707251409055), and the National Key R&D
Program of China under Grant No. 2020AAA0108600.

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