Discourse Parsing of Contentious, Non-Convergent Online Discussions

• URL: http://arxiv.org/abs/2012.04585v1
• Date: Tue, 8 Dec 2020 17:36:39 GMT
• Title: Discourse Parsing of Contentious, Non-Convergent Online Discussions
• Authors: Stepan Zakharov, Omri Hadar, Tovit Hakak, Dina Grossman, Yifat Ben-David Kolikant, Oren Tsur
• Abstract summary: Inspired by the Bakhtinian theory of Dialogism, we propose a novel theoretical and computational framework. We develop a novel discourse annotation schema which reflects a hierarchy of discursive strategies. We share the first labeled dataset of contentious non-convergent online discussions.
• Score: 0.16311150636417257
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online discourse is often perceived as polarized and unproductive. While some conversational discourse parsing frameworks are available, they do not naturally lend themselves to the analysis of contentious and polarizing discussions. Inspired by the Bakhtinian theory of Dialogism, we propose a novel theoretical and computational framework, better suited for non-convergent discussions. We redefine the measure of a successful discussion, and develop a novel discourse annotation schema which reflects a hierarchy of discursive strategies. We consider an array of classification models -- from Logistic Regression to BERT. We also consider various feature types and representations, e.g., LIWC categories, standard embeddings, conversational sequences, and non-conversational discourse markers learnt separately. Given the 31 labels in the tagset, an average F-Score of 0.61 is achieved if we allow a different model for each tag, and 0.526 with a single model. The promising results achieved in annotating discussions according to the proposed schema paves the way for a number of downstream tasks and applications such as early detection of discussion trajectories, active moderation of open discussions, and teacher-assistive bots. Finally, we share the first labeled dataset of contentious non-convergent online discussions.

Related papers

• 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)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing [0.6599344783327052]
  We present a unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances. Our model achieves results comparable with SOTA, without using label collocation and without training a unique architecture/model for each label.
  arXiv  Detail & Related papers  (2023-05-21T17:04:21Z)
• Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation [51.22712675266523]
  Dialogue Topic (DTS) plays an essential role in a variety of dialogue modeling tasks. We propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data.
  arXiv  Detail & Related papers  (2023-05-04T11:35:23Z)
• CluCDD:Contrastive Dialogue Disentanglement via Clustering [18.06976502939079]
  A huge number of multi-participant dialogues happen online every day. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions. We propose a model named CluCDD, which aggregates utterances by contrastive learning.
  arXiv  Detail & Related papers  (2023-02-16T08:47:51Z)
• Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification [26.973476248983477]
  Implicit discourse relations bind smaller linguistic units into coherent texts. We explore the utility of pre-trained sentence embeddings as base representations in a neural network for implicit discourse relation sense classification.
  arXiv  Detail & Related papers  (2022-10-20T04:17:03Z)
• DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances [18.199473005335093]
  This paper presents DialogBERT, a novel conversational response generation model that enhances previous PLM-based dialogue models. To efficiently capture the discourse-level coherence among utterances, we propose two training objectives, including masked utterance regression. Experiments on three multi-turn conversation datasets show that our approach remarkably outperforms the baselines.
  arXiv  Detail & Related papers  (2020-12-03T09:06:23Z)
• 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)
• Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology [68.8836704199096]
  Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpusbased conversational agents comes the need to classify and filter out malevolent responses. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence.
  arXiv  Detail & Related papers  (2020-08-21T22:43:27Z)

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.