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MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines

MultiWOZ 2.0 (Budzianowski et al., 2018) is a recently released multi-domain dialogue dataset spanning 7 distinct domains and containing over 10,000 dialogues. Though immensely useful and one of the largest resources of its kind to-date, MultiWOZ 2.0 has a few shortcomings. Firstly, there are substantial noise in the dialogue state annotations and dialogue utterances which negatively impact the performance of state-tracking models. Secondly, follow-up work (Lee et al., 2019) has augmented the original dataset with user dialogue acts. This leads to multiple co-existent versions of the same dataset with minor modifications. In this work we tackle the aforementioned issues by introducing MultiWOZ 2.1. To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset. This correction process results in changes to over 32% of state annotations across 40% of the dialogue turns. In addition, we fix 146 dialogue utterances by canonicalizing slot values in the utterances to the values in the dataset ontology. To address the second problem, we combined the contributions of the follow-up works into MultiWOZ 2.1. Hence, our dataset also includes user dialogue acts as well as multiple slot descriptions per dialogue state slot. We then benchmark a number of state-of-the-art dialogue state tracking models on the MultiWOZ 2.1 dataset and show the joint state tracking performance on the corrected state annotations. We are publicly releasing MultiWOZ 2.1 to the community, hoping that this dataset resource will allow for more effective models across various dialogue subproblems to be built in the future.


Introduction
In task-oriented conversational systems, dialogue state tracking refers to the problem of estimating a user's goals and requests at each turn of a dialogue. The state is typically defined by the underlying ontology of the domains represented in a dialogue, and a system's job is to learn accurate distributions for the values of certain domainspecific slots in the ontology. There have been a number of public datasets and challenges released to assist in building effective dialogue state tracking modules (Williams et al., 2013;Henderson et al., 2014;Wen et al., 2017). One of the largest resources of its kind is the MultiWOZ 2.0 dataset, which spans 7 distinct task-oriented domains including hotel, taxi, and restaurant booking among others (Budzianowski et al., 2018). This dataset has been a unique resource, in terms of its multi-domain interactions as well as slot value transfers between these domains, and has quickly attracted researchers for dialogue state tracking (Nouri and Hosseini-Asl, 2018;Goel et al., 2019;Wu et al., 2019) and dialogue policy learning (Zhao et al., 2019). Though the original MultiWOZ 2.0 dataset comes with fine-grained dialogue state annotations for all the domains at the turn-level, in practice we have found substantial noise in the annotations of dialogue state values. While some amount of noise in annotations cannot be avoided, it is desirable to have clean data so the error patterns in various models can be attributed to model mistakes rather than the data. To this end, we re-annotated states in the MultiWOZ 2.0 dataset with a different set of interannotators. We specifically accounted for 4 kinds of common mistakes in Mul-tiWOZ 2.0, detailed in Section 2.1. In addition, we also corrected spelling errors and canonicalized entity names as detailed in Section 2.3. Recently there have been a number of extensions to the original MultiWOZ 2.0 dataset that have added additional annotations such as user dialogue act information (Lee et al., 2019). We added this information to our version of the dataset so that it has both system and user dialogue acts. Additionally, we added slot-descriptions for all the dialogue state slots present in the dataset, motivated by recent work on low-resource and zero-shot natural language understanding tasks (Bapna et al., 2017;Shah et al., 2019;Rastogi et al., 2019). Post-correction, we ran state-of-the-art dialogue state tracking models on the corrected data to provide competitive baselines for this new dataset. With this work, we release the corrected and consolidated MultiWOZ 2.0 which we call MultiWOZ 2.1, as well as baselines consisting of stateof-the-art dialogue state tracking techniques on this new data. In Section 2. we provide details for the data correction process and provide examples and statistics on the corrections. We then detail the slot descriptions we added in Section 3. In Section 4., we provide the dialogue act statistics for the user dialogue acts included in our dataset. We detail our baseline models in Section 5. We discuss the performance on this new dataset in Section 6.

Dataset Corrections
The original MultiWOZ 2.0 dataset was collected using a Wizard-of-OZ setup (Kelley, 1984) whereby conversations were conducted between two crowdworkers, one playing the role of the Wizard and the other playing the User. The 6279  none  dontcare  2011  none  yes  1159  none  hotel  1049  dontcare  none  920 none centre User was provided with a goal (for e.g. 'book a hotel and a taxi to the hotel') and interacted with the Wizard with a textbased chat interface to achieve his goal. In the course of a conversation, the Wizard had access to a graphical-userinterface connected to a backend database, and they were expected to annotate state information in user utterances using both drop-down menus and free-form text inputs. The use of free-form text inputs made it so that the values annotated by the Wizard were not guaranteed to be consistent with the underlying database ontology. This, combined with mistakes made by the crowd-workers resulted in several types of annotation mistakes which we outline below.

Dialogue State Error Types
The most common errors types in the original dialogue state annotations include the following: • Delayed markups. These refer to slot values that were annotated one or more turns after the value appeared in the user utterances. Row 1 of Table 3 shows this case where the "Turkish" value appears one turn late in the MultiWOZ 2.0 dialogue.
• Multi-annotations. The same value is annotated as belonging to multiple slots, usually one of these is correct and the other one is spurious. Row 2 of Table 3 shows such a case where "belf" is spurious.
• Mis-annotations. The value is annotated as belonging to a wrong slot type. In row 3 of Table 3 we can see a case where "Thursday" appears in a wrong slot.
• Typos. The value is annotated, but it includes a typo or is not canonicalized. Row 4 of Table 3 exhibits such a case with "centre" misspelled.
• Forgotten values. The slot value never occurs in the dialogue state, even though it was mentioned by the user. Row 5 of Table 3 is an example where "dontcare" is never seen in the data.

Dialogue State Corrections
Our corrections were of two types: manual corrections and automated corrections. Manual corrections involved asking annotators to go over each dialogue turn-by-turn and correcting mistakes detected in the original annotations. During this step, we noticed that sometimes the dialogue state could include multiple values, and hence we annotated them as such.  domain databases provided as part of the corpus. Row 6 of Table 3 shows one such example. We also present some of the most frequent corrections for state values in Table 1. Table 4 presents statistics on the types of corrections made. Due to our canonicalization and reannotation, the vocabulary sizes of many of the slots decreased significantly (Table 2) except 2 slots -"train-leaveAt" and "train-arriveBy". For these slots we noticed that there were times missing in the dialogue states (such as "20:07") which our annotations additionally introduced. We also canonicalized all times in the 24:00 format.

Dialogue Utterance Corrections
It is often the case when building dialogue state systems that the target slot values are mentioned verbatim in the di-    alogue history. Many copy-based dialogue state tracking models heavily rely on this assumption (Goel et al., 2018). In these situations, it is crucial that the slot values are represented correctly within the user and system utterances. However, because dialogue datasets are often collected via crowdsourced platforms where workers are asked to provide utterances via free-form text inputs, these slot values within the utterances may be misspelled or they may not be consistent with the true values from the ontology.
To detect potential error cases within the utterances, for every single dialogue turn, we computed the terms that have Levenshtein distance less than 3 from the slot values annotated for that turn. We then performed string matching for these terms within the turn, forming a set of error candidates. This created a candidate set of 225 potential errors which we then manually inspected to filter out those candidates which were false positives, leaving a collection of 67 verified errors. We then programmatically scanned the entire dataset applying corrections to the verified errors, changing 146 total utterances. As an example of a corrected utterance: "I'm leaving from camgridge and county folk museum." was changed to "I'm leaving from cambridge and county folk museum." Without such a correction, it would be very difficult for a span-based copy mechanism to correctly identify the slot value "cambridge and county folk museum" in its original form.

Dialogue act annotation
MultiWOZ 2.0 has annotations for the system dialogue acts but lacks annotation for the user utterances in the dialogue. (Lee et al., 2019) released a version of the dataset with additional annotations for user dialogue acts. They performed this annotation automatically using heuristics that track the dialogue state, user goal, user utterance, system response and system dialogue act. We used their annotation pipeline to annotate our dataset with dialogue acts.  list the statistics of user and system dialogue acts present in the dataset, respectively.

Baseline Models
Within dialogue state tracking, there are two primary classes of models: f ixed vocabulary and open vocabulary. In f ixed vocabulary models, the state tracking mechanism operates on a predefined ontology of possible slot values, usually defined to be the values seen in the training and validation data splits. These models benefit from being able to fluidly predict values that aren't present in a given dialogue history but suffer from the rigidity of having to define the potentially large slot value list per domain during the model training phase. By contrast open vocabulary models are able to flexibly extract slot values from a dialogue history but struggle to predict slot values that have not been seen in the history. In order to benchmark performance on our updated dataset, we provide joint dialogue state accuracies for a number of f ixed vocabulary and open vocabulary models which are reported in Table 4.
For the models, the dialogue history up to turn n is defined as (u 1 , s 1 , u 2 , s 2 , ..., u n−1 , s n−1 , u n ), where u i and s i are the user and system utterances at turn i respectively. Note that this history also includes the n th user utterance. The Flat Joint State Tracker refers to a bidirectional LSTM network that encodes the full dialogue history and then applies a separate feedforward network to the encoded hidden state for every single state slot. In practice this amounts to 37 separately branching feedforward networks that are trained jointly. The Hierarchical Joint State Tracker incorporates a similar architecture but instead encodes the history using a hierarchical network in the vein of (Serban et al., 2016). TRADE is a recently proposed model that achieved state-of-the-art results on the original MultiWOZ 2.0 data, using a generative state tracker with a copy mechanism (Wu et al., 2019). The DST Reader is a newly pro-  posed model that frames state tracking as a reading comprehension problem, learning to extract slot values as spans from the dialogue history . The HyST is another new model which combines a hierarchical encoder f ixed vocabulary system with an open vocabulary n-gram copy-based system (Goel et al., 2019).

Results and Discussion
As we can see from

Conclusion
We publicly release state corrected MultiWOZ 2.1 and rerun competitive state tracking baselines on this dataset. The dataset will be available on the MultiWOZ Github repository 1 . We hope that the cleaner data allows for better model and performance comparisons on the task of multi-domain dialogue state tracking as well as other dialogue subproblems.  Table 10: * Appendix A: Percentage of changes in dialogue state values before and after annotations. The highest number of changed values are in name slots (e.g., restaurant-name, attraction-name, and hotel-name). Such slots had particularly large numbers of spelling mistakes (e.g., shanghi family restaurant to shanghai family restaurant). Note that while the number of changes to individual slots is small, we ended up changing the joint dialogue state for over 40% of dialogue turns.