Findings on Conversation Disentanglement

• URL: http://arxiv.org/abs/2112.05346v1
• Date: Fri, 10 Dec 2021 05:54:48 GMT
• Title: Findings on Conversation Disentanglement
• Authors: Rongxin Zhu, Jey Han Lau, Jianzhong Qi
• Abstract summary: We build a learning model that learns utterance-to-utterance and utterance-to-thread classification. Experiments on the Ubuntu IRC dataset show that this approach has the potential to outperform the conventional greedy approach.
• Score: 28.874162427052905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and conversation summarization. Framing it as a utterance-to-utterance classification problem -- i.e. given an utterance of interest (UOI), find which past utterance it replies to -- we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utterance-to-utterance and utterance-to-thread classification. Observing that the ground truth label (past utterance) is in the top candidates when our model makes an error, we experiment with using bipartite graphs as a post-processing step to learn how to best match a set of UOIs to past utterances. Experiments on the Ubuntu IRC dataset show that this approach has the potential to outperform the conventional greedy approach of simply selecting the highest probability candidate for each UOI independently, indicating a promising future research direction.

Related papers

• Improve Retrieval-based Dialogue System via Syntax-Informed Attention [46.79601705850277]
  We propose SIA, Syntax-Informed Attention, considering both intra- and inter-sentence syntax information. We evaluate our method on three widely used benchmarks and experimental results demonstrate the general superiority of our method on dialogue response selection.
  arXiv  Detail & Related papers  (2023-03-12T08:14:16Z)
• A Graph-Based Context-Aware Model to Understand Online Conversations [3.8345539498627437]
  In online conversations, comments and replies may be based on external context beyond the immediately relevant information. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection.
  arXiv  Detail & Related papers  (2022-11-16T20:51:45Z)
• Conversation Disentanglement with Bi-Level Contrastive Learning [26.707584899718288]
  Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. We propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space.
  arXiv  Detail & Related papers  (2022-10-27T08:41:46Z)
• TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization [27.185068253347257]
  We build a large-scale (11M) pretraining dataset called RCS based on the multi-person discussions in the Reddit community. We then present TANet, a thread-aware Transformer-based network. Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency plays an essential role in understanding the entire conversation.
  arXiv  Detail & Related papers  (2022-04-09T16:08:46Z)
• 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)
• Probing Task-Oriented Dialogue Representation from Language Models [106.02947285212132]
  This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way.
  arXiv  Detail & Related papers  (2020-10-26T21:34:39Z)
• 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)
• Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization [72.54873655114844]
  Text summarization is one of the most challenging and interesting problems in NLP. This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment.
  arXiv  Detail & Related papers  (2020-10-04T20:12:44Z)
• Exploiting Unsupervised Data for Emotion Recognition in Conversations [76.01690906995286]
  Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations. The available supervised data for the ERC task is limited. We propose a novel approach to leverage unsupervised conversation data.
  arXiv  Detail & Related papers  (2020-10-02T13:28:47Z)
• 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)
• Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method [1.599072005190786]
  We propose a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model. We use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refines the topics for the conversation.
  arXiv  Detail & Related papers  (2020-06-05T10:24:43Z)

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.