ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)
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| Challenge: | Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process. |
| Approach: | They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context. |
| Outcome: | The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations. |
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| Challenge: | Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply . |
| Approach: | They propose a model that matches a response with its multi-turn context using attention. |
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How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
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Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
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Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)
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| Challenge: | Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances. |
| Approach: | They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model . |
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Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
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| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
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Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
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EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts (2025.acl-long)
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Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer
| Challenge: | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks, but efficient processing of long contexts remains a significant challenge. |
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Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
| Approach: | They propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics and use contextual dependencies to improve performance. |
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RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
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ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
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