Papers with multi-turn response selection
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
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| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems (2021.naacl-main)
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| Challenge: | Existing methods to select the correct response for a dialogue system are generation-based and retrieval-based. |
| Approach: | They propose a fine-grained post-training method that reflects the characteristics of the multi-turn dialogue. |
| Outcome: | The proposed model achieves state-of-the-art with significant margins on three benchmark datasets. |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
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| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |
Attend, Select and Eliminate: Accelerating Multi-turn Response Selection with Dual-attention-based Content Elimination (2023.findings-acl)
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| Challenge: | Pre-trained language models can be used to perform multi-turn response selection, but they can be expensive. |
| Approach: | They propose a framework and a strategy that progressively selects and eliminates unimportant content under context-response dual-attention. |
| Outcome: | The proposed method can effectively speed-up SOTA models without much performance degradation and shows a better trade-off between speed and performance than previous methods. |
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. |