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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12500 |
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| _version_ | 1866909141875818496 |
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| author | Sato, Shiki Akama, Reina Suzuki, Jun Inui, Kentaro |
| author_facet | Sato, Shiki Akama, Reina Suzuki, Jun Inui, Kentaro |
| contents | Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models' contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12500 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems Sato, Shiki Akama, Reina Suzuki, Jun Inui, Kentaro Computation and Language Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models' contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods. |
| title | A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.12500 |