SlowGuess/NAACL_2022_OCR huggingface dataset:
Yifan Wang*, Jing Zhao, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He  
JD AI Research, Beijing, China
{wangyifan15,zhaojing857,baojunwei,duanchaoqun1,wuyouzheng1,xiaodong.he}  $@$  jd.com
Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a SLot-TUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets. $^{1}$
Dialogue State Tracking (DST) refers to the task of estimating the dialogue state (i.e., user's intents) at every dialogue turn, where the state is represented in forms of a set of slot-value pairs (Williams et al., 2016; Eric et al., 2019). DST is crucial to the success of a task-oriented dialogue system as the dialogue policy relies on the estimated dialogue state to choose actions. Traditional DST approaches assume that all candidate slot-value pairs are predefined in an ontology (Mrkšić et al., 2017; Zhong et al., 2018; Lee et al., 2019). Then, they scores all possible pairs and selecting the value with the highest score as the predicted value of a slot.
Sys: There are lots to choose from. What type of cuisine are you looking for?
User: I do not care. It needs to be on the south side and moderately priced.
State: restaurant-area=south; pricerange=moderate
Sys: There are 2 options, pizza hut cherry hinton and restaurant alimentum. Can I book you for those?
User: Yes please. I also need a hotel with at least 3 stars and free parking near by the restaurant.
State: hotel-parking=yes; hotel-stars =3
Sys: I am sorry, there is no guest house that meets those criteria, either. Would you like to try a different rating, or a different area?
User: Sure, what about in the city centre?
State: hotel-area = centre; hotel-type = guest house
$\otimes$  : hotel-area=south; hotel-type=guest house
Table 1: An example of DST. "User" and "Sys" means user query and system response respectively. "State" is the golden label of dialogue state. " $\bigotimes$ " denotes the predicted states of some existing models and the state marked red is the incorrect prediction.
DST encounters many challenging phenomena unique to dialogue, such as co-references and ellipsis. Consequently, most of existing DST approaches exploit all dialogue utterances in history to assign value for each slot (Shan et al., 2020; Chen et al., 2020a; Quan and Xiong, 2020; Hu et al., 2020; Chen et al., 2020b). However, this could lead to the incorrect value assignment due to the ambiguous contents introduced from some irrelevant utterances with the current slot. As the example shown in Table 1, the models estimate a slot value "south" for the slot "hotel-area" at turn-3 yet its corresponding golden label is "centre". The reason is that both "south" and "centre" are the potential slot values to the area-related slot (i.e., "restaurant-area" and "hotel-area") in the ontology. Actually, the domain of the utterance at turn-1 is "restaurant" that is irrelevant to the slot "hotel-area".
To address the problem aforementioned, we propose LUNA, a SLot-TUrN Alignment enhanced
† Corresponding author: baojunwei001@gmail.com
$^{1}$ Our code is available at https://github.com/nlper27149/LUNA-dst
Human Language Technologies, pages 3319 - 3328
approach, which divides DST into two sub-tasks: (1) explicitly aligns each slot with its most relevant utterance, (2) assigns the slot value according to the aligned utterance. For example, when predicting the slot value of "hotel-area", LUNA first aligns it with the relevant utterance (i.e., turn-3) and then only uses the representations of this utterance to match slot value. Concretely, LUNA consists of four parts: an utterance encoder, a slot encoder, a value encoder, and an alignment module between the first two encoders. The core of LUNA is the alignment module directed at accurate alignment, otherwise there may be a risk of the failure of the second sub-task. Correspondingly, the alignment module equipped in LUNA is performed by an iteratively bi-directional feature fusion network based on the attention mechanism. Some previous works have explored the feature fusion of the two encoders, but they are all uni-directional (Shan et al., 2020; Chen et al., 2020b; Ye et al., 2021), e.g., turn-to-slot or slot-to-turn. Compared with them, the bi-directional way can build a mutual relevance between two encoders and thus more effective for our alignment-oriented objective.
Additionally, we design a ranking-based auxiliary task to supervise LUNA to learn the slot order along with the conversational flow, which could facilitate the alignment. For example, the order of the slots in Table 1 is:
(1) "restaurant-area" (2) "pricerange" (3) "hotel-parking"
(4) "hotel-stars" (5) "hotel-area" (6) "hotel-type"
Among the above slots, the most difficult-aligned slot is "hotel-area" which confronts the confusion from the utterances at turn-1 (containing "south") and turn-3 (containing "center"). But the remaining five slots are easy-aligned, such as "hotel-stars". If the model combines two information: (1) "hotel-stars" is aligned with the utterance at turn-2, (2) the conversation order of "hotel-area" is after "hotel-stars", it can easily inference that "hotel-area" should be aligned with the utterance at turn-3. Notably, our proposed auxiliary task enables LUNA to learn the semantic correlations as well as the temporal correlations among slots. Whereas, existing DST approaches only attempt to model the semantic correlations (Ye et al., 2021; Zhu et al., 2020; Chen et al., 2020b).
Comprehensive experiments are conducted and the results show that LUNA achieves state-of-the-art (SOTA) on three of the most actively studied datasets: MultiWOZ 2.0 (Budzianowski et al., 2018), MultiWOZ 2.1 (Eric et al., 2019), and Mul
tiWOZ 2.2 (Zang et al., 2020) with joint accuracy of  $55.31\%$ ,  $57.62\%$ , and  $56.13\%$ . The results outperform the previous SOTA by  $+0.97\%$ ,  $+1.26\%$ , and  $+4.43\%$ , respectively. Furthermore, a series of subsequent ablation studies demonstrate the effectiveness of each module in our model. Our main contributions are summarized as follows:
(1) We propose a DST approach LUNA which mitigates the problem of incorrect value assignment through explicitly aligning each slot with its most relevant utterance.
(2) We propose an auxiliary task to facilitate the alignment which is firstly introduced in DST to take the temporal correlations among slots into account.
(3) Empirical experiments are conducted to show that LUNA achieves SOTA results with significant improvements.
DST is a necessary component in task-oriented dialogue systems and a large amount of work has been proposed to achieve better performance. All these methods can be broadly divided into two categories: classification (Xu and Hu, 2018; Zhong et al., 2018; Ren et al., 2018; Xie et al., 2018) and generation (Wu et al., 2019; Hosseini-Asl et al., 2020; Kim et al., 2020). The classification method requires that all possible slot-value pairs are given in a predefined ontology. Then, the pair with the highest score is the final prediction. Conversely, the generation way does not rely on manual definition, which generates dialogue states from utterances using the seq2seq fashion. This work is mainly related to the classification method.
Recently, transformer-based pre-trained models, such as BERT (Devlin et al., 2019), have achieved remarkable results in a range of natural language processing tasks. Thereupon, the research of DST has been shifted to building new models on top of the powerful pre-trained language models. SUMBT (Lee et al., 2019) is the first model to employ BERT to model the relationships between slots and dialogue utterances through a slot-word attention mechanism. CHAN (Shan et al., 2020) presents a hierarchical attention network which uses slot-word attention and slot-turn attention to enhance the representations of slots. All the methods mentioned above predict the value of each slot separately and ignore the correlations among slots. SST (Chen et al., 2020b) incorporates graph at
tention networks into DST and proposes schema graphs which contain slot relations in edges. STAR (Ye et al., 2021) provides a slot self-attention mechanism to learn mutual guidance among slots and enhance the ability to deduce appropriate slot values from related slots. Recently, BORT (Sun et al., 2022) proposes a reconstruction mechanism which enhances the performance of DST.
To the best of our knowledge, we are the first to reveal that exploiting all dialogue utterances to assign value may cause suboptimal results and the first to learn the temporal correlations among slots.
Suppose that there is a conversation composed of  $T$  utterances,  $\mathcal{X} = \{(Q_1,R_1),\dots,(Q_T,R_T)\}$  and a predefined slot set  $S = \{S_{1},\ldots ,S_{J}\}$ , where  $Q_{t}$  denotes the user query at  $t$ -th utterance,  $R_{t}$  is the corresponding system response and  $J$  is the total number of slots. DST aims to predict states at each turn with given utterances up-to-now  $(Q_{\leq t},R_{\leq t})$ , and presents them as slot-value pairs,  $\mathcal{B}_t = \{(S_1,V_1^t),\dots,(S_J,V_J^t)\}$ , where  $S_{j}$  is the  $j$ -th slot in  $S$ , and  $V_{j}^{t}$  is the value with respect to  $S_{j}$  for the  $t$ -th turn. Since the datasets are collected from multi domains, following previous works (Hu et al., 2020; Kim et al., 2020), we concatenate domain names and slot names as domain specific slots.
To tackle this task, we propose the LUNA model. As depicted in Figure 1, this model consists of three encoders and an alignment network. In this section, we will elaborate each module of this model.
3.1 Encoders
Inspired by the success of the pre-trained model in the community of the NLP, we adopt the BERT (Devlin et al., 2019) to implement the context encoder.
3.1.1 Utterance Encoder
Given the  $t$ -th utterance  $(Q_{t}, R_{t})$  and its history  $(Q_{\leq t}, R_{\leq t})$ , we first concatenate them into a single sequence:  $\mathcal{X}_{t} = Q_{1} \oplus R_{1} \oplus \dots \oplus Q_{t} \oplus R_{t}$ . Following the form of the input of the BERT, we then surround the sequence with two special tokens [CLS] and [SEP]. Given that not all of the slots can be aligned to a specific utterance, we further add an extra token [BLANK] as a placeholder. All of the slots that are not mentioned in the dialogue are aligned to [BLANK]. Finally, the input of the utterance encoder can be denoted as follows:
$$
X _ {t} = [ \mathrm {C L S} ] \oplus \mathcal {X} _ {t} \oplus [ \mathrm {S E P} ] \oplus [ \mathrm {B L A N K} ]. \tag {1}
$$
After obtaining  $X_{t}$ , we feed it into the BERT to learn semantic representations:
$$
\mathbf {H} _ {t} = \operatorname {B E R T} _ {\text {f i n e t u n e}} (X _ {t}), \tag {2}
$$
where  $\mathbf{H}_t = [\mathbf{h}_1^t, \mathbf{h}_2^t, \dots, \mathbf{h}_{|X_t|}^t]$ ,  $\mathbf{h}_j^t \in \mathbb{R}^d$ . Additionally, we add a learned embedding to every token indicating which turn it belongs to. Thus, for a given token in  $X_t$ , its input representation is constructed by summing the corresponding token, position, segment, and turn embeddings. In order to make the BERT more adapt to this task, we fine tune the parameters of the BERT during the training stage.
3.1.2 Slot and Value Encoders
Following previous works (Shan et al., 2020; Ye et al., 2021), we leverage another BERT to encode slots and their candidate values. Formally, given a slot  $S_{j}$  or a value  $V_{j}^{t}$ , we first tokenize it into a sequence and then concatenate it with the special token [CLS] to build the input for the slot or value encoder. After that, we exploit the BERT to encode the concatenation as follows:
$$
\mathbf {h} _ {s _ {j}} = \operatorname {B E R T} _ {\text {f i x e d}} \left(S _ {j}\right), \tag {3}
$$
$$
\mathbf {h} _ {v _ {j} ^ {t}} = \operatorname {B E R T} _ {\text {f i x e d}} \left(V _ {j} ^ {t}\right). \tag {4}
$$
We regard the representation of [CLS] as that of the whole slot or value. Specially, since the quantity of the sub-vocabulary related to slots and values are small, we freeze the parameters of BERT in slot and value encoders during the training stage.
3.2 Alignment Module
As mentioned above, DST model usually adopts all of previous utterances as the history to enhance the representation of the current utterance. Although this mechanism enriches the semantic representation, it introduces some noisy and causes confusion for value prediction to a specific slot. To alleviate this issue, the proposed LUNA model adopts iteratively bi-directional feature fusion layers, turn-to-slot and slot-to-turn, to align slots to utterances and provide more relevant utterance for value prediction.
3.2.1 Turn-to-Slot Alignment
In this work, we regard utterances and slots as two sequences and aims to align them with each other. To this end, we first employ a multi-head attention mechanism (Vaswani et al., 2017) to assist
Figure 1: The architecture of LUNA. Note that the workflow in this figure is specifically for the first slot  $S_{1}$ .
the slots extracting relevant information from utterances based on the outputs of the utterance and slot encoders:
$$
\mathbf {h} _ {s _ {j}, t} = \operatorname {M u l t i H e a d} \left(\mathbf {h} _ {s _ {j}}, \mathbf {H} _ {t}, \mathbf {H} _ {t}\right), \tag {5}
$$
where MultiHead  $(\cdot ,\cdot ,\cdot)$  denotes the multi-head attention mechanism. Through this operation, we obtain utterance-aware slot representations.
After that, we adopt  $N$  stacked layers to learn the correlation among slots, and each layer consists of a multi-head self-attention mechanism and a position-wise feed-forward network. We denote this module as Slot SA. Formally, the  $n$ -th layer is computed as follows:
$$
\bar {\mathbf {H}} _ {s} ^ {n} = \operatorname {M u l t i H e a d} \left(\hat {\mathbf {H}} _ {s} ^ {n - 1}, \hat {\mathbf {H}} _ {s} ^ {n - 1}, \hat {\mathbf {H}} _ {s} ^ {n - 1}\right), \tag {6}
$$
$$
\hat {\mathbf {H}} _ {s} ^ {n} = \operatorname {F N N} \left(\operatorname {R e L U} \left(\operatorname {F N N} \left(\bar {\mathbf {H}} _ {s} ^ {n}\right)\right) \right. \tag {7}
$$
where  $\hat{\mathbf{H}}_s^1 = [\mathbf{h}_{s_1,t},\dots,\mathbf{h}_{s_J,t}]$ .
3.2.2 Slot-to-Turn Alignment
For utterances, after obtaining the output of the utterance encoder  $\mathbf{H}_t$ , we first slice it into  $t$  segments,  $\mathbf{U} = [\mathbf{U}_1, \dots, \mathbf{U}_t]$  and each segment corresponds to an utterance. We then exploit a hierarchical attention mechanism to model the slot-to-turn alignment. The hierarchical attention mechanism contains two layers. The first layer models the preliminary alignment between an utterance and a slot and we denote it as Single Slot-to-Turn. The other one focuses on the refined alignment through incorporating all
slot information and we represent it as Overall Slot-to-Turn.
As shown in Figure 1, the Single Slot-to-Turn is responsible for extracting token-level information related to a specific slot from each utterance. Take the  $j$ -th slot  $S_{j}$  as an example. Given its representation  $\mathbf{h}_{s_j}$ , we use it to extract most relevant information from each utterance (e.g.,  $i$ -th utterance) via the multi-head attention mechanism:
$$
\bar {\mathbf {U}} _ {i} = \operatorname {M u l t i H e a d} \left(\mathbf {h} _ {s _ {j}}, \mathbf {U} _ {i}, \mathbf {U} _ {i}\right), \tag {8}
$$
where  $\bar{\mathbf{U}}_i$  is a  $d$ -dimension vector and we regard it as slot  $S_{j}$  aware representation for  $i$ -th utterance. Similarly, we obtain slot  $S_{j}$  aware representations for all utterances  $\bar{\mathbf{U}} = [\bar{\mathbf{U}}_1,\dots,\bar{\mathbf{U}}_t]$  with the same operation.
After that, the Overall Slot-to-Turn layer further aligns utterances with slots. Different with existing work (Ye et al., 2021) of encoding the states of the previous turn  $B_{t-1}$  as an information supplement, we first introduce previous alignment information into each utterance by adding alignment embedding:
$$
\hat {\mathbf {U}} _ {i} = \bar {\mathbf {U}} _ {i} + \mathrm {A E} (i), \tag {9}
$$
where AE is embedding matrix indicating whether the slot  $S_{j}$  aligns utterance  $\mathbf{U}_i$  or not at last turn. Then we utilize another multi-head attention module to update utterance representations based on slots information as follows:
$$
\tilde {\mathbf {U}} _ {i} = \operatorname {M u l t i H e a d} \left(\hat {\mathbf {U}} _ {i}, \hat {H} _ {s} ^ {N}, \hat {H} _ {s} ^ {N}\right). \tag {10}
$$
To aggregate context dependency among utterances, we further introduce a multi-head self-attention mechanism to learn the context aware representation for each utterance:
$$
\mathbf {D} = \operatorname {M u l t i H e a d} (\tilde {\mathbf {U}}, \tilde {\mathbf {U}}, \tilde {\mathbf {U}}), \tag {11}
$$
where  $\tilde{\mathbf{U}} = [\tilde{\mathbf{U}}_1,\dots,\tilde{\mathbf{U}}_t],\mathbf{D} = [\mathbf{D}_1,\dots,\mathbf{D}_t]$ .  $\mathbf{D}$  is adopt to predict the alignment distribution over turns for  $S_{j}$  as follows:
$$
p (\cdot | S _ {j}) = \operatorname {s o f t m a x} \left(\mathbf {W} _ {o} \mathbf {D} + b _ {o}\right), \tag {12}
$$
where  $\mathbf{W}_o\in \mathbb{R}^d$  and  $b_{o}$  are trainable parameters. We employ the cross-entropy as the objective function of the alignment and it can be formulated as:
$$
\mathcal {L} _ {\text {a l i g n}} = - \sum_ {j = 1} ^ {J} \log p \left(\left(Q _ {j} ^ {*}, R _ {j} ^ {*}\right) \mid S _ {j}\right), \tag {13}
$$
where  $(Q_j^*, R_j^*)$  is ground-truth utterance aligned to slot  $S_j$ .
3.2.3 Auxiliary Ranking Task
The output of the Slot SA,  $\hat{\mathbf{H}}_s^N = [\hat{\mathbf{h}}_{s_1}^N,\dots,\hat{\mathbf{h}}_{s_J}^N]$ , only contains the information of the semantic correlations among slots. To facilitate the alignment, the model needs the assistance of the temporal correlations among slots. However, slots are naturally disordered or sorted in lexicographic order. Therefore, we design an auxiliary task to guide the model to learn the temporal information of slots. Particularly, we propose an ordering algorithm to determine the slots order with respect to the dialogue utterances, as shown in Algorithm 1. This task aims to minimize the order differences between the disordered slots and our defined-ordered slots and we utilize the ListMLE (Xia et al., 2008) as the objective function. ListMLE is a standard ranking loss and it is computed based on a defined list and a ground-truth list. To compute the loss, we learn a score for each slot (e.g.,  $S_{j}$ ) as follows:
$$
\mathbf {f} _ {s _ {j}} = \operatorname {S i g m o d} \left(\mathbf {W} _ {s} \hat {\mathbf {h}} _ {s _ {j}} ^ {N} + b _ {s}\right), \tag {14}
$$
where  $\mathbf{W}_s$  and  $b_{s}$  are trainable parameters. Given the ground-truth order of slots  $\mathbf{o} = [o_1,\dots,o_J]$  and the corresponding slot list is  $[S_{o_1},\dots,S_{o_J}]$ , the loss function can be formulated as follows:
$$
p (j \mid S _ {o _ {j}}) = \frac {\exp \left(f _ {s _ {o _ {j}}}\right)}{\sum_ {l = j} ^ {J} \exp \left(f _ {s _ {o _ {l}}}\right)}, \tag {15}
$$
$$
\mathcal {L} _ {\text {o r d e r}} = - \log \left(\prod_ {j = 1} ^ {J} p \left(j \mid S _ {o _ {j}}\right)\right). \tag {16}
$$
Algorithm 1 Slots Ordering Algorithm
Input: L: Label slots for a conversation, T: the number of turns in this conversation
Initialize:  $S$ : A list of sorted slots
1: for  $t \in [1, T]$  do
2: Find the label slots  $L_{t} = [l_{t,1},\dots,l_{t,n}]$  of  $t$ -th turn;
3: Sort  $L_{t}$  by slots' lexicographic order;
4: for  $l$  in  $L_{t}$  do
5: Add  $l$  to  $S$ ;
6: end for
7: end for
8: Define the list of remaining not-aligned slots is  $L_{blank}$ ;
9: Sort  $L_{blank}$  by slots' lexicographic order;
10: for  $l$  in  $L_{blank}$  do
11: Add  $l$  to  $S$ ;
12: end for
3.3 Value Prediction
Above sections describe the method of aligning slots with utterances. We then predict the value for a specific slot based on the most relevant utterance instead of all of the utterances.
Formally, given a slot  $S_{j}$ , we first select the most relevant utterance  $(Q_{j}^{*}, R_{j}^{*})$  as follows:
$$
\left(Q _ {j} ^ {*}, R _ {j} ^ {*}\right) = \arg \max  \left(\left\{p \left(\left(Q _ {i}, R _ {i}\right) \mid S _ {j}\right) \right\} _ {i = 1} ^ {t}\right). \tag {17}
$$
Then we feed its representation  $D^{*} \in D$  into a linear layer which is followed by a layer normalization:
$$
O ^ {*} = \operatorname {L a y e r N o r m} \left(\operatorname {L i n e a r} \left(D ^ {*}\right)\right). \tag {18}
$$
Following Ren et al. (2018), we adopt the L2-norm to compute the distance between a slot and a candidate value. Thereby, the value prediction probability distribution can be formulated as follows:
$$
\begin{array}{l} p \left(V _ {j} ^ {t} | \left(Q _ {\leq t}, R _ {\leq t}\right), S _ {j}\right) = \\ \exp \left(- \| O ^ {*} - h _ {v _ {j} ^ {t}} \| _ {2}\right) \tag {19} \\ \overline {{\sum_ {V _ {k} ^ {t} \in V ^ {t}} \exp (- \| O ^ {*} - h _ {v _ {k}} ^ {t} \| _ {2})}}, \\ \end{array}
$$
where  $V^t$  is the set of candidate values of slot  $S_j$  for the  $t$ -th utterance. Finally, the loss function can be defined as:
$$
\mathcal {L} _ {\text {v a l u e}} = - \sum_ {j = 1} ^ {J} \log \left(p \left(V _ {j} ^ {t} \mid \left(Q _ {\leq t}, R _ {\leq t}\right), S _ {j}\right)\right). \tag {20}
$$
Model	MultiWOZ 2.0	MultiWOZ 2.1	MultiWOZ 2.2	Trainable Parameters
Joint	Slot	Joint	Slot	Joint	Slot
Generation models
SOM-DST (Kim et al., 2020)	51.38	-	52.57	-	-	-	113M
TRADE (Wu et al., 2019)	48.60	96.92	45.60	-	45.40†	-	-
TripPy (Heck et al., 2020)	53.51	-	55.32	-	53.52	-	110M
TripPy w/o LM	45.64†	-	44.80†	-	-	-	-
Seq2Seq-DU (Feng et al., 2021)	-	-	56.10	-	54.40	-	220M
SimpleTOD (Hosseini-Asl et al., 2020)	51.37	-	51.89	-	-	-	-
Classification models
DS-DST (Zhang et al., 2020)	-	-	51.31	97.35	51.70	-	-
DST-Picklist (Zhang et al., 2020)	54.39	-	53.30	97.40	-	-	-
CHAN (Shan et al., 2020)	53.06	-	53.38	-	-	-	133M
SST (Chen et al., 2020b)	51.17	-	55.23	-	-	-	-
STAR (Ye et al., 2021)	54.34	-	56.36	97.51	-	-	135M
LUNA	55.31	97.35	57.62	97.96	56.13	97.68	142M
With Data Augmentation
TripPy+ConvBERT (Mehri et al., 2020)	-	-	58.70	-	-	-	-
TripPy+CoCoAug (Li et al., 2020)	-	-	60.53	-	-	-	-
TripPy+SaCLog (Dai et al., 2021)	-	-	60.61	-	-	-	-
Table 2: Joint accuracy (%) and slot accuracy (%) on the test sets. "LM" denotes label map in TripPy.  $\dagger$  indicates the reproduced results using the source codes and remaining results reported in the literature.
We adopt the multi-task learning to jointly optimize the alignment loss, value prediction loss and the auxiliary task loss. The total loss is defined as follows:
$$
\mathcal {L} _ {\text {j o i n t}} = \mathcal {L} _ {\text {o r d e r}} + \mathcal {L} _ {\text {a l i g n}} + \mathcal {L} _ {\text {v a l u e}}
$$
We evaluate our approach on three gradually refined task-oriented dialogue datasets: MultiWOZ 2.0 (Budzianowski et al., 2018), MultiWOZ 2.1 (Eric et al., 2019), and the latest MultiWOZ 2.2 (Zang et al., 2020), containing over 10,000 dialogues, 7 domains, and 35 domain-slot pairs. MultiWOZ 2.1 modifies about  $32\%$  of the state annotations in MultiWOZ 2.0. MultiWOZ 2.2 is the latest and a further refined version of MultiWOZ 2.1, which solves the inconsistency of state updates and some problems of ontology.