| Challenge: | Deliberative dialogue is a structured, face-to-face method of public interaction that is fundamental to the concept of deliberative democracy. |
| Approach: | They propose to use a combination of lexical, sentiment, durational and further ‘derivative’ features of adjacency pairs to train traditional classification models. |
| Outcome: | The proposed method improves the accuracy of classification models and prediction tasks and shows that the task of recognising agreement is demanding but possible. |
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Modeling Deliberative Argumentation Strategies on Wikipedia (P18-1)
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| Challenge: | Existing models for deliberative discussions have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. |
| Approach: | They propose to model argumentation strategies of deliberative discussions by annotating ongoing discussions with a label that can be used for move description. |
| Outcome: | The proposed model can predict arguments of participants in deliberative discussions using metadata from Wikipedia talk pages. |
Developing A Multilabel Corpus for the Quality Assessment of Online Political Talk (2022.lrec-1)
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| Challenge: | a corpus of political tweets labeled for its deliberative characteristics is presented . the dataset offers a first step in building dictionaries to aid in the measurement of the Discourse Quality Index . |
| Approach: | They present a Twitter Deliberative Politics dataset that measures the quality of political tweets . they propose to use machine learning to analyze tweets and to use it to build dictionaries . |
| Outcome: | The proposed dataset is useful to linguists, political scientists, and social scientists . it offers a first step in building dictionaries for the quality assessment of political talk in english . |
Automatic Prediction of Discourse Connectives (L18-1)
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| Challenge: | Discourse connectives are used to bind together and explicate the relation between pieces of text. |
| Approach: | They propose to use a dataset of 2.9M sentence pairs separated by discourse connectives to test their accuracy. |
| Outcome: | The proposed model outperforms the human model in the prediction task . the proposed model has a higher F1 under specific conditions . |
Scaling up Discourse Quality Annotation for Political Science (2022.lrec-1)
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| Challenge: | Existing annotations on deliberative quality are time-consuming and suffer from class imbalance . ephd thesis: deliberation is not only the output of the decision making, but also the discussion that leads up to it. |
| Approach: | They propose to use data augmentation techniques to improve deliberative quality predictions in a standard dataset. |
| Outcome: | The proposed methods outperform classifiers based on linguistic features and argument quality annotations with or without data augmentation. |
Do dialogue representations align with perception? An empirical study (2023.eacl-main)
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| Challenge: | masked language models produce stronger correlations than auto-regressive models, but humans and models make different response selection mistakes. |
| Approach: | They propose to use spoken conversation as a model to measure human comprehension behaviour. |
| Outcome: | The proposed model outperforms the model which produces the strongest correlation with human responses. |
Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue (2026.eacl-long)
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| Challenge: | Prior research on meta-reviewing has treated this as a summarization problem over review reports . prior research demonstrated that decision-makers can be effectively assisted in such scenarios via dialogue agents. |
| Approach: | They propose to use large-scale large-language models to generate synthetic data for meta-reviewing . they then use these data to train dialogue agents tailored for meta review . |
| Outcome: | The proposed method outperforms *off-the-shelf* dialogue agents in meta-reviewing scenarios. |
Promoting Constructive Deliberation: Reframing for Receptiveness (2024.findings-emnlp)
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| Challenge: | Current methods for promoting pro-social discussion and debate online are limited. |
| Approach: | They propose automatic reframing of disagreeing responses to signal receptiveness to a preceding comment. |
| Outcome: | The proposed framework can be used to promote constructive debate and debate online. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
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Deep Learning for Dialogue Systems (C18-3)
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| Challenge: | Using deep learning to build robust and scalable spoken dialogue systems is still a challenging task. |
| Approach: | tutorial focuses on an overview of dialogue system development . goal-oriented spoken dialogue systems are most prominent component in virtual personal assistants . |
| Outcome: | This tutorial focuses on an overview of dialogue system development while summarizing the challenges. |
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |