Multi-Domain Dialogue Acts and Response Co-Generation (2020)

Abstract: Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.

## introduction task-oriented dialogue systems aim to facilitate people with such services as hotel reservation and ticket booking through natural language conversations. recent years have seen a rapid proliferation of interests in this task from both academia and industry @xcite @xcite . a standard architecture of these systems generally decomposes this task into several subtasks, including natural language understanding @xcite , dialogue state tracking @xcite and natural language figure 1 : an example of dialogue from the multiwoz dataset, where the dialogue system needs to generate a natural language response according to current belief state and related database records. figure 1 shows a dialogue example, from which we can notice that the natural language generation subtask can be further divided into dialogue act prediction and response generation @xcite @xcite . while the former is intended to predict the next action(s) based on current conversational state and database information, response generation is used to produce a natural language response based on the action(s). in order for dialogues to be natural and effective, responses should be fluent, informative, and relevant. nevertheless, current sequence-to-sequence models often generate uninformative responses like "i don't know" @xcite , hindering the dialogues to continue or even leading to a failure. some researchers @xcite ., area root hotel restaurant attraction name request inform phone domain action slot dialog act graph sequence generation (ours) multiple binary classification (hdsa) hotel ... inform...phone @xmath0. inform ... area phone 2019) sought to combine multiple decoders into a stronger one to avoid such responses, while others @xcite @xcite represent dialogue acts in a global, static vector to assist response generation. as pointed out by @xcite , dialogue acts can be naturally organized in hierarchical structures, which has yet to be explored seriously. take two acts station-request-stars and restaurantinform-address as an example. while the first act rarely appears in real-world dialogues, the second is more often. moreover, there can be multiple dialogue acts mentioned in a single dialogue turn, which requires the model to attend to different acts for different sub-sequences. thus, a global vector is unable to capture the inter-relationships among acts, nor is it flexible for response generation especially when more than one act is mentioned. to overcome the above issues, we treat dialogue act prediction as another sequence generation problem like response generation and propose a co-generation model to generate them concurrently. unlike those classification approaches, act sequence generation not only preserves the interrelationships among dialogue acts but also allows close interactions with response generation. by attending to different acts, the response generation module can dynamically capture salient acts and produce higher-quality responses. figure 2 demonstrates the difference between the classification and the generation approaches for act prediction. as for training, most joint learning models rely on hand-crafted or tunable weights on development sets @xcite @xcite . the challenge here is to combine two sequence generators with varied vocabularies and sequence lengths. the model is sensitive during training and nontrivial to generate an optimal weight. to address this issue, we opt for an uncertainty loss @xcite to adaptively adjust the weight according to task-specific uncertainty. we conduct extensive studies on a largescale task-oriented dataset to evaluate the model. the experimental results confirm the effectiveness of our model with very favorable performance over several state-of-the-art methods. the contributions of this work include: ## related work dialogue act prediction and response generation are closely related in general in the research of dialogue systems @xcite @xcite , where dialogue act prediction is first conducted and used for response generation. each dialogue act can be treated as a triple (domainaction-slot) and all acts together are represented in a one-hot vector @xcite . such sparse representation makes the act space very large. to overcome this issue, @xcite took into account act structures and proposed to represent the dialogue acts with level-specific one-hot vectors. each dimension of the vectors is predicted by a binary classifier. to improve response generation, @xcite proposed to learn different expert decoders for different domains and acts, and combined them with a chair decoder. @xcite applied a cold-fusion method @xcite to combine their response decoder with a language model. @xcite treated dialogue acts as latent variables and used reinforcement learning to optimize them. reinforcement learning was also applied to find optimal dialogue policies in task-oriented dialogue systems @xcite or obtain higher dialog-level rewards in chatting @xcite . besides, @xcite proposed to predict the acts explicitly with a compact act graph representation and employed hierarchical disentangled self-attention to control response text generation. unlike those pipeline architectures, joint learning approaches try to explore the interactions between act prediction and response generation. a large body of research in this direction uses a shared user utterance encoder and train natural language understanding jointly with dialogue state tracking @xcite . @xcite proposed to train a unified network for two subtasks of dialogue state tracking, i.e., knowledge base operation and response candidate selection. @xcite showed that joint learning of dialogue act and response benefits representation learning. these works generally demonstrate that joint learning of the subtasks of dialogue systems is able to improve each other and the overall system performance. ## architecture let @xmath1, r 1 , . . . , u t-1 , r t-1 , u t } denote the dialogue history in a multi-turn conversational setting, where u i and r i are the i-th user utterance and system response, respectively. @xmath2, d 2 , . . . , d n } includes the attributes of related database records for current turn. the objective of a dialogue system is to generate a natural language response r @xmath3. . . y n of n words based on the current belief state and database attributes. in our framework, dialogue acts and response are co-generated based on the transformer encoderdecoder architecture @xcite . a standard transformer includes a multi-head attention layer that encodes a value v according to the attention weights from query q to key k, followed by a position-wise feed-forward network (g f ): ## further analysis more thorough studies and analysis are conducted in this section, trying to answer three questions: ## human evaluation we conduct a human study to evaluate our model by crowd-sourcing. 4 for this purpose we randomly selected 100 sample dialogues (742 turns in total) from the test dataset and constructed two groups of systems for comparison: marco vs. hdsa and marco vs. human response, where human response means the reference responses. responses generated by each group were randomly assigned in pairs to 3 judges, who ranked them according to their completion and readability @xcite . completion measures if the response correctly answers a user query, including relevance and informativeness. readability reflects how fluent, natural and consistent the response is. the results of this study are shown in figure 7 , where "win", "tie" or "lose" mean our marco system wins over, ties with or loses to its counterpart, respectively. from the results we note that marco outperforms hdsa and human response in completion, and ties 94% with hdsa in readability while underperforming human response. overall speaking, marco is superior to hdsa and comparable with human response. we further analyzed the bad cases of our model in readability and found that our model slightly suffers from token level repetition, a problem that can be solved by methods like the coverage mechanism @xcite . in completion, our model can understand the users' need and tends to provides them more relevant information, so that they can finish their goals in shorter turns. we present two examples in figure 8. in the first example, the user requests the hotel type while hdsa ignores it. the user requests to book one ticket in the second example, yet both hdsa and human response ask about the number once again. in contrast, our model directly answers the questions with correct information. to sum up, marco successfully improves the dialogue system by generating relevant and informative responses. example 1 user: i'm trying to plan a trip there but need a cheap place to stay. . . . system: finches bed and breakfast is cheap. user: okay. sounds good. can you tell me the type of hotel and provide me with the hotel's phone number? results reference: finches is a guesthouse, and their number is 012***53. can i help with anything else today? hdsa: the phone number for finches is 012***53. marco: finches is a guesthouse, and their number is 012***53. example 2 user: i am looking for a train that leaves on thursday going to cambridge. . . . system: tr1149 departs london kings cross at 17:17 and arrives in cambridge at 18:08. will that work? user: yes it will. can you book that for just me and provide the reference number? results reference:sure thing! how many tickets do you need? hdsa: how many tickets would you like me to book for you? marco: i was able to book one ticket for you. your reference number is r57g4du4. figure 8 : two examples to show that marco successfully improves the dialogue system by generating relevant and informative responses. ## conclusion in this paper, we presented a novel co-generation model for dialogue act prediction and response generation in task-oriented dialogue systems. unlike previous approaches, we modeled act prediction as a sequence generation problem to exploit the semantic structures of acts and trained it jointly with response generation via dynamic attention from response generation to act prediction. to train this joint model, we applied an uncertainty loss for adaptive weighting of the two tasks. extensive studies were conducted on a large-scale task-oriented dataset to evaluate the proposed model, and the results confirm its effectiveness with very favorable performance over several state-of-the-art methods.