Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems

• URL: http://arxiv.org/abs/2005.13119v2
• Date: Wed, 22 Sep 2021 08:20:24 GMT
• Title: Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems
• Authors: Zehao Lin, Shaobo Cui, Guodun Li, Xiaoming Kang, Feng Ji, Fenglin Li, Zhongzhou Zhao, Haiqing Chen, Yin Zhang
• Abstract summary: We propose a predictive approach named Predict-then-Decide (PTD) to tackle this Wait-or-Answer problem. We conduct experiments on two real-life scenarios and three public datasets.
• Score: 24.560203199376478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially in real-world industry scenarios, in which the dialogue system is unsure whether it should answer the query of user immediately or wait for further supplementary input. Motivated by such an interesting predicament, we define a novel Wait-or-Answer task for dialogue systems. We shed light on a new research topic about how the dialogue system can be more intelligent to behave in this Wait-or-Answer quandary. Further, we propose a predictive approach named Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer. The decision of decision model is made with the assistance of two ancillary prediction models: a user prediction and an agent prediction. The user prediction model tries to predict what the user would supplement and uses its prediction to persuade the decision model that the user has some information to add, so the dialogue system should wait. The agent prediction model tries to predict the answer of the dialogue system and convince the decision model that it is a superior choice to answer the query of user immediately since the input of user has come to an end. We conduct our experiments on two real-life scenarios and three public datasets. Experimental results on five datasets show our proposed PTD approach significantly outperforms the existing models in solving this Wait-or-Answer problem.

Related papers

• Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users [51.34484827552774]
  We release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios. We propose a novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query.
  arXiv  Detail & Related papers  (2023-10-31T14:12:07Z)
• EM Pre-training for Multi-party Dialogue Response Generation [86.25289241604199]
  In multi-party dialogues, the addressee of a response utterance should be specified before it is generated. We propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels.
  arXiv  Detail & Related papers  (2023-05-21T09:22:41Z)
• A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS [12.060990859604681]
  We propose a proactive interaction mechanism where the system predicts the user satisfaction with the candidate response before giving it to the user. If the user is not likely to be satisfied according to the prediction, the system will ask the user a suitable question to determine the real intent of the user. We deploy and evaluate our model on DuerOS, and observe a 19% relative improvement on the accuracy of user satisfaction prediction and 2.3% relative improvement on user experience.
  arXiv  Detail & Related papers  (2022-12-05T09:17:49Z)
• Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection [0.0]
  We consider high-quality transmission is essentially built based on apprehending the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting implicit user persona.
  arXiv  Detail & Related papers  (2022-04-15T08:12:10Z)
• Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems [109.16553492049441]
  We propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs.
  arXiv  Detail & Related papers  (2022-03-20T17:51:49Z)
• User Response and Sentiment Prediction for Automatic Dialogue Evaluation [69.11124655437902]
  We propose to use the sentiment of the next user utterance for turn or dialog level evaluation. Experiments show our model outperforming existing automatic evaluation metrics on both written and spoken open-domain dialogue datasets.
  arXiv  Detail & Related papers  (2021-11-16T22:19:17Z)
• Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead [5.062869359266078]
  intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. We present a novel look-ahead approach that uses user utterance generation to improve intent prediction in time.
  arXiv  Detail & Related papers  (2021-10-24T11:12:48Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)
• "Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model [24.560203199376478]
  In real human-human conversations, human often sequentially sends several short messages for readability instead of a long message in one turn. We propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
  arXiv  Detail & Related papers  (2020-02-22T04:05:41Z)
• Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems [10.746852024552334]
  This work investigates the embeddings for representing dialog history in spoken language understanding systems. We proposed to integrate dialogue history into an end-to-end signal-to-concept SLU system. Three types of h-vectors are proposed and experimentally evaluated in this paper.
  arXiv  Detail & Related papers  (2020-02-14T13:09:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.