Papers with conversational context
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
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| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation (2022.naacl-srw)
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| Challenge: | Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). |
| Approach: | They propose a graph-based ERC model which considers conversational context and speaker personality. |
| Outcome: | The proposed model outperforms baseline and other graph-based methods on a benchmark dataset. |
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)
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| Challenge: | Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences. |
| Approach: | They propose a method that captures conversational context and user content and behavior preferences. |
| Outcome: | The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets. |
A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)
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| Challenge: | Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships . |
| Approach: | They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context. |
| Outcome: | The proposed model is based on a large human-human conversation corpus with people and location annotations. |
Variational Hierarchical User-based Conversation Model (D19-1)
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| Challenge: | Recent approaches to conversation response generation model speakers and utterances together but are too tailored to the speakers. |
| Approach: | They propose a new conversation model with a stochastic variable conditioned on the speakers and affects the context. |
| Outcome: | The proposed model outperforms existing models in generating appropriate conversation responses. |
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)
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| Challenge: | Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks. |
| Approach: | They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain. |
| Outcome: | The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs. |
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings (2025.coling-main)
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| Challenge: | Prior work has demonstrated the importance of the conversational context in stance detection. |
| Approach: | They propose a multimodal architecture for stance detection that fuses transformer-based content embedding with unsupervised structural embeddment. |
| Outcome: | The proposed model outperforms strong baselines on common benchmarks and outperformed existing models on common frameworks. |
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)
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| Challenge: | Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi . |
| Approach: | They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels . |
| Outcome: | The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation . |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
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| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights (2025.acl-long)
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| Challenge: | Existing approaches to detect abusive language often ignore conversational context, leading to inconsistent and sometimes inconclusive results. |
| Approach: | They propose a graph neural network approach that uses conversational context to model social media conversations as graphs, where nodes represent comments and edges capture reply structures. |
| Outcome: | The proposed model outperforms baseline and linear context-aware methods and achieves significant improvements in F1 scores. |
Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study (2025.emnlp-main)
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| Challenge: | Existing safety evaluations rely on artificial images to evaluate vision-language models . a recent study found that memes are more effective at bypassing safety measures than synthetic or typographic images. |
| Approach: | They propose a benchmark pairing meme images with harmful and benign instructions . they assess multiple VLMs across single and multi-turn interactions . |
| Outcome: | The proposed benchmark pairs real meme images with harmful and benign instructions. |
ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
| Approach: | They propose to augment social media posts with conversational scenarios to reflect the impact of conversational context on toxicity. |
| Outcome: | The proposed model outperforms existing models on social media with conversational scenarios. |