# On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools

Zhoujian Sun1 Zhengxing Huang2&Nai Ding2,1,111Corresponding author 1Zhejiang
Lab
2Zhejiang University<EMAIL_ADDRESS>{zhengxinghuang,
<EMAIL_ADDRESS>
###### Abstract

Dialogue state tracking (DST) is a component of the task-oriented dialogue
system. It is responsible for extracting and managing slot values according to
utterances, where each slot represents a part of the information to accomplish
a task, and slot value is updated recurrently in each dialogue turn. However,
many DST models cannot update slot values appropriately. These models may
repeatedly inherit wrong slot values extracted in previous turns, resulting in
the fail of the entire DST task. They cannot update indirectly mentioned slots
well, either. This study designed a model with a mentioned slot pool (MSP) to
tackle the update problem. The MSP is a slot-specific memory that records all
mentioned slot values that may be inherited, and our model updates slot values
according to the MSP and the dialogue context. Our model rejects inheriting
the previous slot value when it predicates the value is wrong. Then, it re-
extracts the slot value from the current dialogue context. As the contextual
information accumulates, the new value is more likely to be correct. It also
can track the indirectly mentioned slot by picking a value from the MSP.
Experimental results showed our model reached state-of-the-art DST performance
on MultiWOZ datasets222We released the source code of this paper at
https://github.com/ZJLAB-AMMI/msp.

##  1 Introduction

The task-oriented dialogue system is a type of system that aims to collect
information according to a multi-turn dialogue between a user and an agent to
accomplish a task. Dialogue state tracking (DST) is a module of the system
that is responsible for extracting values from utterances to fill slots and
maintaining slots over the continuation of the dialogue, where each slot
represents an essential part of the information and turn-specific values of
all slots comprise the dialogue state Heck et al. (2020); Ni et al. (2021).

Figure 1: Sample DST process. Green, blue, red, and orange slots indicate the
value is updated via current turn utterances, inherited from the previous
turn, wrong, and from another slot, respectively. We used purple to mark key
information in utterances.

Figure 1 describes a sample DST process. As each slot is typically mentioned
only once in the entire dialogue, the dialogue state is updated recurrently.
Therefore, the dialogue state update strategy plays a critical role in the DST
task. However, we found this topic is not detailly investigated. Many previous
studies adopted a naïve update strategy that directly inherits the previous
value when a slot is not mentioned in the current turn Chao and Lane (2019).
Once a model extracts a wrong slot value, the wrong value may be repeatedly
inherited in the following dialogue, resulting in the fail of the entire DST
task, e.g., the train-people slot in the sample Manotumruksa et al. (2021);
Zhao et al. (2021). Furthermore, a slot may be mentioned indirectly in a
complex DST task as the value is referred from another slot rather than
explicitly mentioned in current turn utterances Zhou and Small (2019); Heck et
al. (2020), e.g., the value of restaurant-day slot in the sample is from the
train-day slot. An intelligent model needs to reject inheriting wrong values
from previous turns and correctly track values for indirectly mentioned slots.
Psychological studies have shown that humans can constantly monitor and update
wrong interpretations during language processing. For example, when listening
to the first a few words of a sentence, the listener will build a plausible
interpretation. If this interpretation is inconsistent with later words, the
brain will re-analyze the sentence and adopt a new interpretation that is
consistent with all the input Townsend et al. (2001). Here, we adopt a similar
strategy that allows models to update slot values based on subsequent input.

This study designed a model with an additional mentioned slot pool (MSP)
module to tackle the dialogue state update problem more elaborately. MSP is a
slot-specific memory including all slot values that are possible to be
inherited. For each slot, our model will determine whether to inherit the
previous value or extract the value from utterances according to dialogue
context and the MSP. This design enables the model not to inherit the previous
slot value when it predicates the value is wrong. Then, the model re-extracts
the slot value from current dialogue context. As contextual information
accumulates with dialogue progresses, the new value extraction process is more
likely to find the right value and correct previous mistakes. For example, the
last turn of the sample DST contains the utterance ”six persons as well.” This
contextual information helps the model realize that the values of train-people
and restaurant-people slots should be the same. As the value of the
restaurant-people slot is six, the wrong train-people value may be corrected
in the new value extraction process. Meanwhile, our model can track indirectly
mentioned slot values by picking a value in MSP because all relevant slot
values are integrated into it.

We investigated the performance of our model on three representative DST
datasets. The result showed that our model achieved state-of-the-art (SOTA)
performance among DST models which were not trained by external datasets.
Further analysis also indicated that our design is more efficient than other
dialogue state update methods. We used the abbreviation MSP to denote both the
pool and our model in the following content.

##  2 Related Work

Recently, fine-tuning large pretrained neural network language model (PNNLM)
gradually becomes the de facto standard paradigm to tackle DST tasks Devlin et
al. (2019). For example, Mehri et al. Mehri et al. (2020) fine-tuned BERT
Devlin et al. (2019) to track dialogue state. This type of studies
demonstrated that DST performance could be significantly improved by simply
using larger PNNLM. The potential of the prompt technique also inspired
researchers to fulfill the DST task by giving model slot descriptions Zang et
al. (2020); Liu et al. (2021). Some studies demonstrated the efficiency of
conducting data augmentation. Song et al. Song et al. (2021) and Summerville
et al. Summerville et al. (2020) augmented data by copying utterances and
replacing the slot value label. Li et al. Li et al. (2021) used the pretrained
utterance generator and counterfactual goal generator to create novel user
utterances.

Meanwhile, another series of studies try to improve DST performance by
designing a more effective model structure. Traditional DST models formulate
slot filling as a classification task, requiring a predefined ontology
containing all possible classification values Nouri and Hosseini-Asl (2018).
However, these models suffer from generalization issues. To solve this issue,
Wu et al. Wu et al. (2019) adopted an encoder-decoder framework to formulate
the DST as a machine translation task, and Gao et al. Gao et al. (2019)
formulated DST as a span finding task. Both methods are widely adopted in
subsequent studies, e.g., Tian et al. (2021); Zhou and Small (2019). Previous
studies also realized that the slot value might be mentioned indirectly. Heck
et al. Heck et al. (2020), Kim et al. Kim et al. (2020), and Zhou et al. Zhou
and Small (2019) proposed a triple copy strategy, a selective overwrite
method, and a knowledge evolving graph to deal with the indirect mention
problem, respectively. Manotumruksa et al. Manotumruksa et al. (2021) noticed
the wrong slot value is mistakenly inherited, and they tackle this problem by
amplifying the loss weight of DST on early turns. Although these studies have
tried to solve the mistakenly inherit problem and the indirectly mention
problem independently, none of them try to solve two problems at once, while
we achieved this goal by introducing the model equipped with a MSP.

Figure 2: Model diagram

##  3 Methodology

###  3.1 Preliminaries

Figure 2 depicts the structure of our model. We represent a dialogue as
$X=\\{U_{1}^{a},U_{1}^{u},…,U_{T}^{a},U_{T}^{u}\\}$, where $T$ is the total
turn number, $U_{t}^{a}$ and $U_{t}^{u}$ are utterances of agent and user in
turn $t$, respectively. $U_{t}^{a}$ and $U_{t}^{u}$ consist of two lists of
word tokens $a_{ti}$ and $u_{tj}$, respectively. Dialogue context
$C_{t}=\\{U_{1}^{a},U_{1}^{u},…,U_{t}^{a},U_{t}^{u}\\}$ indicates observable
dialogue utterances at turn $t$. Following the setting of previous work Heck
et al. (2020), we add a classification token $[CLS]$ in front of $C_{t}$, and
feed this extended dialogue context into a PNNLM. We use
$R_{t}=[r_{t}^{CLS},r_{t}^{1},…,r_{t}^{\left\lvert C_{t}\right\rvert}]$ to
denote the output of PNNLM, where $r_{t}^{i}\in\mathbb{R}^{n}$ corresponds to
a token in $C_{t}$ and $n$ represents the output dimension of the PNNLM. The
goal of DST is to exactly predict the dialogue state $y_{t}$ according to
$C_{t}$. $y_{t}$ is a set of slot-value tuples.

We use $\mathcal{M}_{s,t}=[M_{s,t}^{1},...,M_{s,t}^{K}]$ to denote the MSP,
where $K$ is the maximum size of MSP. $M_{s,t}^{i}$ indicates the predicted
values of slot $s$ or relevant slots $s^{\prime}$ at turn $t-1$. The
definition of relevant slots is described later.
$m_{s,t}\in\mathbb{R}^{K\times n}$ and $m_{s,t}^{i}$ indicate the
representation of $M_{s,t}$ and $M_{s,t}^{i}$, respectively. The low-rank
bilinear model is utilized to generate a fused MSP representation Kim et al.
(2018),

$m_{s,t}^{\rm fused}={\rm softmax}([r_{slot}+r_{t}^{CLS}]W_{s}^{\rm
fused}m_{s,t}^{T})m_{s,t}$ (1)

where $r_{slot}\in\mathbb{R}^{n}$ are representations of a given slot, and
$W_{s}^{\rm fused}\in\mathbb{R}^{n\times n}$ is a learnable parameter.

###  3.2 Hit Type Prediction

Each slot is equipped with a hit type prediction layer. At each turn $t$, the
hit type prediction layer maps representations of MSP and dialogue context to
one of the four classes in $\\{none,dontcare,mentioned,hit\\}$,

$p_{s,t}^{\rm type}={\rm softmax}(W_{s}^{\rm type}[m_{s,t}^{\rm
fused}+r_{t}^{CLS}]+b_{s}^{\rm type})\in\mathbb{R}^{4}$ (2)

where none indicates the slot is not mentioned until turn $t$, dontcare
indicates the user does not care about the value of slot $s$, mentioned
indicates slot value is from an item in MSP, and hit indicates slot value
needs to be updated according to $C_{t}$. If a slot is already mentioned and
the predicted slot hit type is hit, it indicates our model predicts the
previous slot value is wrong, and the model will update the slot value via the
hit value prediction module.

###  3.3 Mentioned Value Selection

As described in equation 3, we utilized a bilinear model to select the most
appropriate slot value in MSP according to the representation of $C_{t}$ when
our model assigns mentioned as hit type. The value with biggest $p_{s,t}^{\rm
mention}$ will be selected.

$p_{s,t}^{\rm mention}={\rm softmax}(r_{t}^{CLS}W_{s}^{\rm
mention}m_{s,t}^{T})$ (3)

###  3.4 Hit Value Prediction

Our model extracts a slot value from $C_{t}$ when the model assigns hit as hit
type. In this study, we refer to slots whose possible value number are small
as categorical slots, e.g., whether a hotel has free internet, and slots whose
possible value numbers are large, unenumerable, or may change over time as
span slots, e.g., restaurant name in a booking task. The value of a
categorical slot is predicted via a classification method. A slot-specific
prediction layer takes $r_{t}^{CLS}$ and $m_{s,t}^{\rm fused}$ as input and
generate the probabilities of each slot value,

$p_{s,t}^{\rm hit}={\rm softmax}(W_{s}^{\rm hit}[m_{s,t}^{\rm
fused}+r_{t}^{CLS}]+b_{s}^{\rm hit})\in\mathbb{R}^{\left\lvert
V_{s}\right\rvert}$ (4)

where $V_{s}$ denotes the ontology of a categorical slot. We predict the value
of a span slot by finding a token span within $C_{t}$. Our model determines
the token span by predicting its start token index and end token index. A
slot-specific span prediction layer takes $R_{t}$ as input and projects it as:

$[\alpha_{s,t}^{i},\beta_{s,t}^{i}]=W_{s}^{\rm hit}r_{t}^{i}+b_{s}^{\rm
hit}\in\mathbb{R}^{2}$ (5) $p_{s,t}^{\rm start}={\rm softmax}(\alpha_{s,t})$
(6) $p_{s,t}^{\rm end}={\rm softmax}(\beta_{s,t})$ (7)

The index with the biggest probability will be assigned as the classify value,
start index, or end index. The span will be assigned as none if the start
index is larger than the end index.

###  3.5 Optimization

The loss function for the hit type prediction, mentioned value selection, and
hit value prediction of a single dialogue are defined as follows:

$\mathcal{L}_{\rm type}=\sum_{t}^{T}\sum_{s}^{\mathcal{S}}{-{\rm
log}(y_{s,t}^{\rm type}(p_{s,t}^{\rm type})^{T})}$ (8) $\mathcal{L}_{\rm
mention}=\sum_{t}^{T}\sum_{s}^{\mathcal{S}}{-{\rm log}(y_{s,t}^{\rm
mention}(p_{s,t}^{\rm mention})^{T})}$ (9) $\mathcal{L}_{\rm
hit}=\sum_{t}^{T}\sum_{s}^{\mathcal{S}}\left\\{\begin{array}[]{lr}-{\rm
log}(y_{s,t}^{\rm hit}(p_{s,t}^{\rm hit})^{T})\ (\rm categorical\ slot)\\\
-\frac{1}{2}({\rm log}(y_{s,t}^{\rm start}(p_{s,t}^{\rm start})^{T})+\\\ {\rm
log}(y_{s,t}^{\rm end}(p_{s,t}^{\rm end})^{T}))\ (\rm span\ slot)\\\
\end{array}\right.$ (10)

where $y_{s,t}^{\rm type}$, $y_{s,t}^{\rm mention}$, $y_{s,t}^{\rm hit}$,
$y_{s,t}^{\rm start}$, $y_{s,t}^{\rm end}$ are one-hot encoded labels of a
slot hit type, mentioned slot, categorical slot, and the start index and the
end index of a span slot, respectively. The joint loss function of dialogue is
a weighted sum of $\mathcal{L}_{\rm type}$, $\mathcal{L}_{\rm mention}$, and
$\mathcal{L}_{\rm hit}$, as shown in equation 11, where $\alpha$, $\beta$,
$\gamma$ are weight hyperparameters.

$\mathcal{L}=\alpha\mathcal{L}_{\rm type}+\beta\mathcal{L}_{\rm
mention}+\gamma\mathcal{L}_{\rm hit}$ (11)

##  4 Experiments

###  4.1 Experiment Settings

####  4.1.1 Dataset

We conducted experiments on three annotated DST datasets, i.e., MultiWOZ 2.1,
MultiWOZ 2.2, and WOZ 2.0, respectively Eric et al. (2020); Zang et al.
(2020); Wen et al. (2017). We preprocessed datasets following Heck et al.
(2020). We mainly focus on analyzing the results of MultiWOZ 2.1 and 2.2
because they are by far the most challenging open-source datasets in DST task.
MultiWOZ 2.1 and 2.2 are comprised of over 10,000 multi-domain dialogues over
a large ontology. There are five domains (train, restaurant, hotel, taxi,
attraction), with 30 domain-slot pairs appearing in all data portions. We also
report experimental results on the WOZ 2.0 to add additional evidence,
although the it is smaller than the MultiWOZ dataset in both ontology and the
number of examples.

####  4.1.2 Range of Mentioned Slot Pools

For a slot $s$ at turn $t$, the MSP is comprised of the value of slot $s$ and
values of (at most) other three relevant slots $s^{\prime}$ at turn $t-1$. The
none slot value is not included. We define the $s^{\prime}$ is a relevant slot
of $s$ if $s$ may inherit the value of slot $s^{\prime}$. Of note, a slot only
inherit the value from a small fraction of other slots. For example, the taxi-
destination slot cannot inherit the value from the restaurant-food slot and
taxi-departure slot because the restaurant-food is not a place, and the
destination cannot be the same as the departure. We designed a custom
dictionary in this study to define the range of relevant slots. The MSP will
be padded if its actual size is less than four. We used the masking method to
avoid the model selecting the padded value. The MSP will be truncated if its
actual size is larger than four. Only the latest four updated slot values will
be reserved. If the actual size of MSP is zero and our model assigns the slot
hit type as mentioned, the slot value will be assigned as none.

####  4.1.3 Evaluation Metric

We mainly evaluated DST models using the Joint Goal Accuracy (JGA) metric.
Turn-specific JGA is one if and only if all slot-value pairs are correctly
predicted, otherwise zero. The general JGA score is averaged across all turns
in the test set.

Although JGA is the most widely used metric in the DST task, it is not
comprehensive enough because the label distribution in the DST dataset is
highly imbalanced. We adopted precision, recall, and F1 to investigate model
performance more detailly. As slot filling is not a binary classification
task, we define $\rm precision=\frac{TP}{(TP+FP)}$, $\rm
recall=\frac{TP}{(TP+FN+PLFP)}$ , and F1 is the harmonic mean of recall and
precision. TP (true positive) indicates the number of cases that the slot
value is not none, and the model successfully predicts the value. FP (false
positive) indicates that the slot value is none, but the model predicts not
none. FN (false negative) indicates that the slot value is not none, but the
model predicts none. PLFP (positive label false prediction) indicates that the
slot value is not none and the model predicts a wrong positive value.

####  4.1.4 Implemention Details

We used the pre-trained BERT transformer as the PNNLM backbone Devlin et al.
(2019), which was also adopted in most previous DST studies. The base version
of BERT was trained on lower-uncased English text. It has 12 hidden layers
with 768 units and 12 self-attention heads. The large version has 24 hidden
layers with 1024 units and 16 self-attention heads, and it was trained on
cased English text. The base and large versions of BERT have about 110 million
and 345 million parameters, respectively. Unless specified, we used the base
version of BERT as the pre-trained backbone and reported corresponding
performance.

The maximum input sequence length was set to 512 tokens after tokenization.
The weights $\alpha$, $\beta$, and $\gamma$ were 0.6, 0.2, and 0.2,
respectively. We adopted embeddings released from WordPiece as value
representations and slot representations $(m_{s,t}^{i},r_{slot})$ Wu et al.
(2016). The word embeddings were locked during the training process. If the
slot and the value need to be represented by multi-tokens, we used the mean of
the corresponding token embeddings as the representation.

For optimization, we used Adam optimizer Kingma and Ba (2015). The initial
learning rate was set to 1e$-$5, and the total epoch number was set to 20. We
conducted training with a warmup proportion of 10% and let the learning rate
decay linearly after the warmup phase. Early stopping was employed based on
the JGA of the development set. All the reported performance JGA were the mean
of five independent experiments.

####  4.1.5 Baseline Models

We compared our proposed model with a variety of recent DST baselines.

  * •

TRADE Wu et al. (2019) encodes the whole dialogue context using bidirectional
Gated Recurrent Units (GRU) and generates the value for every slot using the
GRU-based copy mechanism.

  * •

SUMBT Lee et al. (2019) learns the relations between domain-slot-types and
slot-values appearing in utterances through attention mechanisms based on
contextual semantic vectors.

  * •

DS-DST Zhang et al. (2020) is an ontology-based DST model that requires an
ontology with all possible values for each domain-slot pair.

  * •

Trippy Heck et al. (2020) uses the triple copy mechanism to track the dialogue
state.

  * •

Seq2Seq-DU Feng et al. (2021) employs two encoders to encode the utterances
and the descriptions of schemas and a decoder to generate pointers to
represent the state of dialogue.

  * •

AG-DST Tian et al. (2021) generates a dialogue based on the current turn and
the previous dialogue state and a two-pass process.

As our model is fine-tuned on the target dataset, we did not include models
trained by augmented or external corpus as baselines to make the comparison
fairly, e.g. Mehri et al. (2020); Li et al. (2021). The performance of
baselines was cited from corresponding papers or Zhao et al. (2021).

###  4.2 Experimental Results

####  4.2.1 DST Performance

Table 1 describes the DST performance of our MSP models and baselines in
MultiWOZ 2.1, MultiWOZ 2.2, and WOZ 2.0 datasets, respectively. The domain-
specific JGAs of two MultiWOZ datasets are described in Table 2. The MSP-B
indicated the model used base version of BERT as the backbone, while the MSP-L
indicated the model used the large version of BERT. The AG-DST-S and AG-DST-T
indicates the two models used single PNNLM and two PNNLMs as backbones,
respectively. The doamin-specific JGA indicated our MSP model obtained better
performance in taxi, resaurant, and attraction task because of the update of
MultiWOZ dataset.

| # of | MultiWOZ | WOZ
---|---|---|---
Model | Para. | 2.1 | 2.2 | 2.0
TRADE | / | 45.6% | 45.4% | /
SUMBT | 110M | 49.2% | 49.7% | 91.0%
DS-DST | 110M | 51.2% | 51.7% | 91.2%
Trippy | 110M | 55.3% | 50.7% | 92.7%
MSP-B | 110M | 56.2% | 54.2% | 91.2%
Seq2Seq-DU | 220M | 56.1% | 54.4% | /
AG-DST-S | 340M | / | 56.2% | /
AG-DST-A | 680M | / | 57.1% | /
MSP-L | 345M | 57.2% | 57.7% | /
Table 1: Main results Model | MultiWOZ 2.1 | MultiWOZ 2.2
---|---|---
Taxi | 96.2% | 97.5%
Restaurant | 88.4% | 88.8%
Hotel | 85.0% | 82.2%
Attraction | 89.1% | 89.3%
Train | 89.5% | 87.6%
Table 2: Domain-specific JGA of MSP

As the size of PNNLM significantly influences the performance of models in
almost all natural language processing tasks, it is necessary to figure out
whether the performance improvement of a model is from its structure design or
its PNNLM scale. Therefore, we also described the number of parameters in
PNNLM. The result showed that our MSP-B model achieved better performance than
baselines when their PNNLM sizes were similar. Specifically, the MSP-B model
improved SOTA JGA of MultiWOZ 2.1 from 55.3% to 56.2% (compared to Trippy) and
MultiWOZ 2.2 from 51.7% to 54.2% (compared to DS-DST). It also achieved
comparable performance (JGA: 91.2%) compared to DS-DST and SUMBT in WOZ 2.0,
though slightly worse than Trippy (JGA: 92.7%).

Our MSP model is also more efficient than baselines because it achieved
comparable or better performance with significantly fewer parameters and
without utilizing the slot description information. Specifically, the MSP-B
model obtained 56.2% and 54.2% JGA in two MultiWOZ datasets via only about 110
million parameters (one uncased-base BERT). The Seq2Seq-DU achieved similar
performance via about 220 million parameters (two uncased-base BERTs) and the
schema descriptions (JGA: 56.1% and 54.4% in two MultiWOZ datasets).
Similarly, the MSP-L model achieved significantly better performance than AG-
DST (JGA: 57.7% vs. 56.2% in MultiWOZ 2.2) when using PNNLMs with a similar
number of parameters. The AG-DST model is slightly worse than our MSP model.
Even it uses two times more parameters (JGA: 57.7% vs. 57.1% in MultiWOZ 2.2
dataset). Meanwhile, our MSP-L model achieved 57.2% JGA in MultiWOZ 2.1
dataset. As far as we know, our MSP model reached a new SOTA in the MultiWOZ
dataset among models not trained by external or augmented datasets.

Model | MultiWOZ 2.1 | MultiWOZ 2.2
---|---|---
Pure context | 53.7% | 52.3%
Changed state | 54.9% | 53.2%
Full state | 55.5% | 53.6%
MSP | 56.2% | 54.2%
Table 3: Update strategy comparison

####  4.2.2 Update Strategy Comparison

We conducted experiments on our strategies and three common strategies to
investigate whether our MSP-based dialogue update strategy is better. The
three strategies are:

  * •

Pure context strategy. This strategy does not use the previous dialogue state
and tracking the dialogue state purely relies on dialogue context. It is
widely used in end-to-end models, e.g., Hosseini-Asl et al. (2020).

  * •

Changed state strategy. This strategy utilizes the entire dialogue context to
track slots changed in the latest turn. If a slot is not mentioned in the
latest turn, it inherits the value recorded in the previous dialogue state.
Heck et al. Heck et al. (2020) and Zhang et al. Zhang et al. (2020) used this
strategy.

  * •

Full state strategy. This strategy converts previous dialogue state into a
string, and utilizes the dialogue context and dialogue state string to track
entire dialogue state. We adopted the design of AG-DST to implement this
strategy Tian et al. (2021).

Table 3 describes the result of the dialogue state update strategy comparison,
where all other experimental settings are the same. It is not surprising that
the performance of the changed state strategy is better than the pure context
strategy (JGA: 54.9% vs. 53.7% in MultiWOZ 2.1 and 53.2% vs. 52.3% in MultiWOZ
2.2) as dialogue state is a compact representation of the dialogue history.
Moreover, our strategy achieved about 2% and 1% improvement compared to
changed state strategy and full state strategy as it achieved JGA as 56.2% and
54.2% in MultiWOZ 2.1 and 2.2 datasets, respectively. These results
demonstrated that our MSP-based dialogue state update strategy is more
effective in DST tasks.

####  4.2.3 Ablation Study

We conducted ablation studies to investigate the performance of our model in
five different structures.

  * •

Span-short context. All slots are predicted via the span-based method, and the
model tracks the dialogue state purely based on the latest 128 tokens in the
dialogue context.

  * •

Long context. The model tracks dialogue state based on the latest 512 tokens.

  * •

Categorical slots. The categorical slot is predicted via the classification-
based method in this structure.

  * •

MSP-self. Adding the MSP module into the model. Only the previous value of the
target slot is included in the MSP.

  * •

MSP-full. Our design. Previous values of the target slot and relevant slots
are included in the MSP.

Table 4 describes the result of the ablation study. The model with a long
context reached significantly better performance than the model with a short
context, demonstrating that DST models benefit from longer contextual
information. Applying the classification-based method to track categorical
slots also improves DST performance. These findings accord with findings of
previous studies Tian et al. (2021); Zhou and Small (2019).

The DST model obtained about 2%-3% performance improvement by equipping the
MSP module. The MSP-full model obtained faint performance improvement compared
to the MSP-self model (JGA: 56.2% vs. 56.0%, 54.2% vs. 53.9% in two MultiWOZ
datasets, respectively). On the one hand, these results showed the
effectiveness of updating the dialogue state via our MSP-based strategy. On
the other hand, it indicates that integrating the value of another slot into
the MSP is helpful, though the performance gain is not significant. The
ablation study demonstrated that the MSP module could be used as an additional
model component to improve the DST performance.

Model | MultiWOZ 2.1 | MultiWOZ 2.2
---|---|---
Span-short context | 46.4% | 47.9%
+Long context | 52.8% | 52.0%
+Categorical slots | 54.6% | 53.3%
+MSP-self | 56.0% | 53.9%
+MSP-full | 56.2% | 54.2%
Table 4: Abalation study

####  4.2.4 Inherit Analysis

Figure 3: Inappropriate inherit analysis Figure 4: Indirect mentioned slot
inherit analysis

The previous three subsections have demonstrated the effectiveness of our
model. In this subsection, we will further investigate the correctness of our
assumption. That is, whether the MSP module improves DST performance by
rejecting inheriting wrong slot values and tracking the indirectly mentioned
slots.

Figure 3 describes inappropriate inherit analysis result of the MSP, changed
state, and full state based models on an experiment conducted in the MultiWOZ
2.2 dataset. Error count means the number of wrong slot value prediction
cases. Inherit error means the error is caused by inappropriate inheriting. We
defined inheriting the wrong previous slot value or failing to track
indirectly mentioned slots as inappropriate inheriting. Revision success
indicates the model rejects inheriting a wrong value and revising it into a
correct value. The MSP model achieved better performance as it mistakenly
predicted 5,266 slots, 218 times less than the changed state model. Meanwhile,
the inappropriate inherit count of MSP is also less than the changed state
model about 200 times. The MSP model successfully corrected the previous
mistake 386 times, about 10% more than the changed state model. These results
indicate that performance improvement of the MSP model is likely partially
from rejecting inherited wrong previous values. Result of full state model
also derived the similar conclusion.

Figure 4 describes the result of indirect mentioned slot inherit analysis on
the same experiment. It indicates the MSP model tracked indirectly mentioned
slots 612 times, occupying about 91% of indirectly mentioned cases, while we
cannot investigate this ability of other models for their black box properity.
Although we did not found appropriate baselines to proof the superiority of
MSP model in inheriting indirectly mentioned slots, its superiority could be
verified indirectly. Of note, the MSP-full model achieved slightly better
performance than the MSP-self model. The only difference between the two
models is that the MSP-full model contains information of indirectly mentioned
slots, while the MSP-self model does not. Therefore, we can reasonably infer
that the performance improvement of the MSP-full model is from the
incorporation of indirectly mentioned slots. It likely improves the model’s
ability to handle indirectly mentioned slots, resulting in the JGA
improvement.

####  4.2.5 Error Analysis

Slot | Slot Type | Accuracy | Precision | Recall | F1 | TP | TN | FP | FN | PLFP
---|---|---|---|---|---|---|---|---|---|---
taxi-leaveat | span | 98.9% | 81.6% | 92.6% | 86.8% | 3.6% | 95.4% | 0.3% | 0.5% | 0.3%
taxi-destination | span | 98.1% | 82.1% | 92.5% | 87.0% | 6.4% | 91.7% | 0.5% | 0.4% | 1.0%
taxi-depature | span | 97.9% | 79.1% | 93.0% | 85.5% | 6.1% | 91.8% | 0.5% | 0.4% | 1.2%
taxi-arriveby | span | 99.1% | 84.4% | 87.1% | 85.7% | 2.7% | 96.5% | 0.4% | 0.3% | 0.2%
restaurant-name | span | 94.8% | 84.9% | 92.0% | 88.3% | 19.8% | 75.0% | 1.7% | 2.9% | 0.6%
hotel-name | span | 95.8% | 87.9% | 90.7% | 89.3% | 17.7% | 78.1% | 1.8% | 1.9% | 0.5%
hotel-parking | categorical | 95.4% | 82.4% | 87.4% | 84.8% | 12.9% | 82.5% | 1.9% | 2.5% | 0.2%
hotel-type | categorical | 95.4% | 82.4% | 87.4% | 84.8% | 12.9% | 82.5% | 1.9% | 2.5% | 0.2%
attraction-name | span | 93.1% | 72.0% | 75.2% | 73.6% | 9.6% | 83.6% | 3.2% | 2.9% | 0.8%
train-leaveat | span | 97.1% | 82.9% | 97.0% | 89.4% | 12.4% | 84.6% | 0.4% | 1.5% | 1.0%
Table 5: Error distribution

At last, we investigated weaknesses of our model. Table 5 describes the slot-
specific error distribution of a MultiWOZ 2.2 dataset experiment. We only
showed ten slots whose F1 values are less than 90% to save space. These slots
are the main performance bottleneck of DST tasks. It is not surprising that
most slots are span slots because finding an exact correct token span in a
dialogue with hunderends of tokens is difficult. We found the model idenfity
wrong slot values mainly because of FP predictions and FN predictions, which
is not surprising as well. However, the error distribution revealed that the
performance of many slots lagged because of the high PLFP rate. More than half
of the mistakes are PLFP in taxi-destination and taxi-departure slot, and over
30% of mistakes are PLFP in the train-leaveat slot. Previous studies have
noticed this phenomenon, but they did not analyze it Tian et al. (2021).

We detailly investigated the high PLFP rate problem in this study. It seems
that most PLFP mistakes occur in cases that require the model to identify the
correct value in several candidate values. For example, when a user says, “I
need a train leaving after 19:45.” and the agent replies, “There is a train
leaving at 21:00.”, there are two candidate values for the train-leaveat slot,
i.e., “19:45”, and “21:00”. We found our model may predict “19:45”, rather
than the correct “21:00”. This result reflected that our model understands
shallow semantic information because it extracted a time token span rather
than a meaningless one. However, it still cannot understand the deep semantic
information because its prediction was wrong.

##  5 Conclusion

This study proposed a model with a MSP to improve DST performance. The
experimental results indicate our model reached new SOTA in DST tasks in
MultiWOZ 2.1 and 2.2 datasets. Further experiments demonstrated the MSP can be
used as an addidtional component to improve the DST performance, and the MSP-
based dialogue state update strategy is more effective than other common
update strategies. Meanwhile, we quantitatively analyzed that our design
indeed helps the model reject wrong values and track indirectly mentioned
slots. However, our model still performs poorly in understanding deep semantic
information. In the future study, we will integrate external grammar knowledge
to improve the model’s understanding ability in complex dialogue context.

## Acknowledgements

This research was supported by the National Key Research and Development
Program of China (No. 2021ZD0204105), Exploratory Research Project of Zhejiang
Lab (No. 2022RC0AN01), and Open Research Projects of Zhejiang Lab (No.
2019KD0AD01/012).

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