Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

• URL: http://arxiv.org/abs/2012.07410v1
• Date: Mon, 14 Dec 2020 10:58:01 GMT
• Title: Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
• Authors: Xiuying Chen, Zhi Cui, Jiayi Zhang, Chen Wei, Jianwei Cui, Bin Wang, Dongyan Zhao, Rui Yan
• Abstract summary: In multi-turn dialog, utterances do not always take the full form of sentences. We propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question.
• Score: 49.92173751203827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In multi-turn dialog, utterances do not always take the full form of sentences \cite{Carbonell1983DiscoursePA}, which naturally makes understanding the dialog context more difficult. However, it is essential to fully grasp the dialog context to generate a reasonable response. Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog. Enlightened by the multi-task learning scheme, we propose a joint framework that unifies these two tasks, sharing the same encoder to extract the common and task-invariant features with different decoders to learn task-specific features. To better fusing information from the question and the dialog history in the encoding part, we propose to augment the Transformer architecture with a memory updater, which is designed to selectively store and update the history dialog information so as to support downstream tasks. For the experiment, we employ human annotators to write and examine a large-scale dialog reading comprehension dataset. Extensive experiments are conducted on this dataset, and the results show that the proposed model brings substantial improvements over several strong baselines on both tasks. In this way, we demonstrate that reasoning can indeed help better response generation and vice versa. We release our large-scale dataset for further research.

Related papers

• Enhancing Multimodal Query Representation via Visual Dialogues for End-to-End Knowledge Retrieval [26.585985828583304]
  We propose an end-to-end multimodal retrieval system, Ret-XKnow, to endow a text retriever with the ability to understand multimodal queries. To effectively learn multimodal interaction, we also introduce the Visual Dialogue-to-Retrieval dataset automatically constructed from visual dialogue datasets. We demonstrate that our approach not only significantly improves retrieval performance in zero-shot settings but also achieves substantial improvements in fine-tuning scenarios.
  arXiv  Detail & Related papers  (2024-11-13T04:32:58Z)
• Enhancing Visual Dialog State Tracking through Iterative Object-Entity Alignment in Multi-Round Conversations [3.784841749866846]
  We introduce Multi-round Dialogue State Tracking model (MDST) MDST captures each round of dialog history, constructing internal dialogue state representations defined as 2-tuples of vision-language representations. Experimental results on the VisDial v1.0 dataset demonstrate that MDST achieves a new state-of-the-art performance in generative setting.
  arXiv  Detail & Related papers  (2024-08-13T08:36:15Z)
• CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation [38.60073402817218]
  Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. We present a new framework for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models.
  arXiv  Detail & Related papers  (2023-03-02T09:27:11Z)
• Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems [8.419582942080927]
  Response generation is one of the critical components in task-oriented dialog systems. We propose an approach that performs textit dynamic prompting where the prompts are learnt from dialog contexts. We show that contextual dynamic prompts improve response generation in terms of textit combined score citemehri-etal 2019-structured by 3 absolute points.
  arXiv  Detail & Related papers  (2023-01-30T20:26:02Z)
• Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues [34.78482218571574]
  We propose a data augmentation method to automatically augment high-quality responses with different semantics by counterfactual inference. Experimental results show that our data augmentation method can augment high-quality responses with different semantics for a given dialogue history, and can outperform competitive baselines on multiple downstream tasks.
  arXiv  Detail & Related papers  (2022-10-30T13:26:49Z)
• SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding [68.94808536012371]
  We propose a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora. Our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks.
  arXiv  Detail & Related papers  (2022-09-14T13:42:50Z)
• Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
  How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
  arXiv  Detail & Related papers  (2022-08-16T08:21:12Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation [80.45816053153722]
  DialogVED introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation.
  arXiv  Detail & Related papers  (2022-04-27T16:18:15Z)

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.