Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure

• URL: http://arxiv.org/abs/2104.08601v1
• Date: Sat, 17 Apr 2021 17:46:00 GMT
• Title: Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
• Authors: Lu Ji, Jing Li, Zhongyu Wei, Qi Zhang, Xuanjing Huang
• Abstract summary: We identify the responding relations in the conversation discourse, which link response utterances to their initiations. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.
• Score: 53.77234444565652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts, such as 79 vs. 73 MRR on Chinese customer service dialogues. We further probe into our outputs and shed light on how topics and discourse indicate conversational user interactions.

Related papers

• Joint Learning of Context and Feedback Embeddings in Spoken Dialogue [3.8673630752805446]
  We investigate the possibility of embedding short dialogue contexts and feedback responses in the same representation space using a contrastive learning objective. Our results show that the model outperforms humans given the same ranking task and that the learned embeddings carry information about the conversational function of feedback responses.
  arXiv  Detail & Related papers  (2024-06-11T14:22:37Z)
• Modeling the Quality of Dialogical Explanations [21.429245729478918]
  We study explanation dialogues in terms of the interactions between the explainer and explainee. We analyze the interaction flows, comparing them to those appearing in expert dialogues. We encode the interaction flows using two language models that can handle long inputs.
  arXiv  Detail & Related papers  (2024-03-01T16:49:55Z)
• Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark [10.378163772785204]
  We propose a teacher-student framework based on hierarchical contrastive learning to predict the topic shift without the response. The experimental results on our Chinese CNTD and English TIAGE show the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2023-05-02T04:03:50Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension [61.55950233402972]
  We propose a new key utterances extracting method for dialogue reading comprehension. It performs prediction on the unit formed by several contiguous utterances, which can realize more answer-contained utterances. As a graph constructed on the text of utterances, we then propose Question-Interlocutor Scope Realized Graph (QuISG) modeling.
  arXiv  Detail & Related papers  (2022-10-26T04:00:42Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations [23.214585012203084]
  This work studies automatic quotation generation in an online conversation. An encoder-decoder neural framework is employed to continue the context with a quotation. Experiment results on two large-scale datasets in English and Chinese.
  arXiv  Detail & Related papers  (2021-06-18T03:38:48Z)
• Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge [43.352833140317486]
  Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. This work makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension.
  arXiv  Detail & Related papers  (2021-02-10T15:00:12Z)
• Response Selection for Multi-Party Conversations with Dynamic Topic Tracking [63.15158355071206]
  We frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. We propose a novel multi-task learning framework that supports efficient encoding through large pretrained models. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks.
  arXiv  Detail & Related papers  (2020-10-15T14:21:38Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

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.