| 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 . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three multi-turn conversation benchmarks including an e-commerce dialogue corpus. |
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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. |
| Approach: | They propose to use a turn-aware context modeling layer to adapt existing models . they propose to model multi-turn contexts from the perspective of sequential relationship, local relationship, and query-alike manner . |
| Outcome: | The proposed method can be adapted to several advanced response selection models. |
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)
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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. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for retrieval-based dialogues concatenate all turns in the dialogue history as input, ignoring dialogue dependency and structural information between the utterances. |
| Approach: | They propose a multi-grained conversational graph network that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. |
| Outcome: | The proposed method improves on two benchmarks on open domain dialogues. |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
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| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
| Challenge: | Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular. |
| Approach: | They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses. |
| Outcome: | The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset. |
Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection (2020.coling-main)
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| Challenge: | Existing methods for multi-turn response selection are not practical as the turns of conversations vary. |
| Approach: | They propose to use latent interaction modeling to model multi-level interactions between utterance and response. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three multi-turn response selection benchmark datasets. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables (P19-1)
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| Challenge: | Existing work on multi-turn conversations has focused on the relationship between the response and context, but it is lacking a model to model the relationship. |
| Approach: | They propose a conversational semantic relationship RNN model to construct hierarchical dependency between utterances and their context. |
| Outcome: | The proposed model significantly improves the quality of responses in terms of fluency, coherence, and diversity compared to baseline methods. |
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension (P19-1)
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| Challenge: | Existing research on multi-turn spoken conversations focuses on reading comprehension of passages . interactivity of spoken content can cause lower information density and topic diffusion . |
| Approach: | They propose a hierarchical attention neural network architecture to improve spoken dialogue comprehension by combining turn-level and word-level attention mechanisms. |
| Outcome: | The proposed approach outperforms baseline attention models and is robust to lengthy and out-of-distribution test samples. |