Related papers: With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations

With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations

URL: http://arxiv.org/abs/2402.02837v1
Date: Mon, 5 Feb 2024 09:48:07 GMT
Title: With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations
Authors: Amandine Decker (LORIA, UL, CNRS, SEMAGRAMME, GU), Maxime Amblard (SEMAGRAMME, LORIA)
Abstract summary: This paper tries to reach a comparable level of accuracy as recent machine learning based topic segmentation models. The features we identify as meaningful for this task help us understand better the topical structure of a conversation.
Score: 0.565395466029518
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the structure of dialogue beyond the sequence of utterances. However, studying this high-level structure is a complex task that we try to approach by first segmenting dialogues into smaller topically coherent sets of utterances. Understanding the interactions between these segments would then enable us to propose a model of topic organisation at a dialogue level. In this paper we work with open-domain conversations and try to reach a comparable level of accuracy as recent machine learning based topic segmentation models but with a formal approach. The features we identify as meaningful for this task help us understand better the topical structure of a conversation.

Related papers

Unsupervised Mutual Learning of Dialogue Discourse Parsing and Topic Segmentation [38.956438905614256]
rhetorical structure and topic structure are mostly modeled separately or with one assisting the other in the prior work. We propose an unsupervised mutual learning framework of two structures leveraging the global and local connections between them. We also incorporate rhetorical structures into the topic structure through a graph neural network model to ensure local coherence consistency.
arXiv Detail & Related papers (2024-05-30T08:10:50Z)
Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
arXiv Detail & Related papers (2023-09-18T11:03:55Z)
Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
arXiv Detail & Related papers (2023-06-06T19:17:47Z)
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)
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling [61.67321200994117]
In a customer service system, dialogue summarization can boost service efficiency by creating summaries for long spoken dialogues. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries. We propose a novel topic-augmented two-stage dialogue summarizer ( TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues.
arXiv Detail & Related papers (2020-12-14T07:50:25Z)
Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
arXiv Detail & Related papers (2020-09-26T08:43:06Z)
Structured Attention for Unsupervised Dialogue Structure Induction [110.12561786644122]
We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
arXiv Detail & Related papers (2020-09-17T23:07:03Z)
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning [50.5572111079898]
Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
arXiv Detail & Related papers (2020-02-27T04:36:52Z)

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