| Challenge: | a topic model that incorporates structural relationships connecting documents in socially generated corpora is of limited application in the sciences. |
| Approach: | They propose a topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. |
| Outcome: | The proposed model captures discursive interactions along observed reply links and integrates latent distributed representations in a deep architecture. |
Similar Papers
A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks for dialogic discourse parsing are not suitable for contentious discussions . authors propose a model for non-convergent discourse paring that does not require label collocation . |
| Approach: | They propose a multi-label scheme for contentious dialog parsing that uses multiple labels . they propose combining embeddings of the utterance, context and the labels through GRN layers . |
| Outcome: | The proposed model achieves comparable results with SOTA without label collocation and without training a unique architecture/model for each label. |
Structured Representation Learning for Online Debate Stance Prediction (C18-1)
Copied to clipboard
| Challenge: | Existing models for understanding debate dialog ignore relationships between different topics and focus on textual content and user interaction. |
| Approach: | They propose to view this task as a representation learning problem and embed the text and authors jointly based on their interactions. |
| Outcome: | The proposed model can achieve significantly better results compared to competing models. |
One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling (2023.findings-emnlp)
Copied to clipboard
Ruoxue Ma, Jiarong Xu, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing studies on integrating online community to solve social problems have not fully utilized these three components and the relationship among them. |
| Approach: | They propose a framework that simultaneously considers communities, users, and texts and can easily connect with a variety of downstream tasks related to social media. |
| Outcome: | The proposed model can be used to perform violation detection, sentiment analysis, and community recommendation across multiple tasks. |
Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey (2025.emnlp-main)
Copied to clipboard
Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé, Danai Myrtzani, Theodoros Evgeniou, Ion Androutsopoulos, John Pavlopoulos
| Challenge: | Recent advances in LLMs enable artificial facilitation agents to not only moderate content, but also actively improve the quality of interactions. |
| Approach: | They propose a taxonomy on discussion quality evaluation and a new taxonomies for intervention and facilitation strategies. |
| Outcome: | The proposed methods synthesize ideas from Natural Language Processing (NLP) and Social Sciences to provide a taxonomy on discussion quality evaluation, and a roadmap of good practices and future research directions. |
Issue Framing in Online Discussion Fora (N19-1)
Copied to clipboard
| Challenge: | In online discussion fora, speakers often make arguments by highlighting certain aspects of the topic. |
| Approach: | They propose to use a newswire and social media annotated corpus to detect issue frames in online discussions. |
| Outcome: | The proposed model can be applied to the domain of discussion fora using multi-task and adversarial training. |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
Copied to clipboard
| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions. |
| Approach: | They propose a dataset of Reddit posts annotated across four causal tasks . they use a binary causal classification, explicit vs. implicit causality, cause–effect span extraction and causal gist generation to bridge causal detection and reasoning over informal discourse. |
| Outcome: | The proposed dataset analyzes 10,120 Reddit posts discussing public health related to the COVID-19 pandemic. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
PhraseCTM: Correlated Topic Modeling on Phrases within Markov Random Fields (P18-2)
Copied to clipboard
| Challenge: | Recent phrase-level topic models are unable to capture the correlation structure among the discovered topics. |
| Approach: | They propose a phrase-level topic model PhraseCTM and a method to find out the correlations of topics at phrase level. |
| Outcome: | The proposed method shows that correlated topic modeling is a good way to interpret themes of corpus. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
Copied to clipboard
| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
| Approach: | They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context. |
| Outcome: | The proposed model improves parser performance in a social media context without pre-identified discourse units. |