@inproceedings{sato-etal-2022-n,
title = "N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models",
author = "Sato, Shiki and
Akama, Reina and
Ouchi, Hiroki and
Tokuhisa, Ryoko and
Suzuki, Jun and
Inui, Kentaro",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.60",
doi = "10.18653/v1/2022.sigdial-1.60",
pages = "637--644",
abstract = "Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.",
}
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%0 Conference Proceedings
%T N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
%A Sato, Shiki
%A Akama, Reina
%A Ouchi, Hiroki
%A Tokuhisa, Ryoko
%A Suzuki, Jun
%A Inui, Kentaro
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F sato-etal-2022-n
%X Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.
%R 10.18653/v1/2022.sigdial-1.60
%U https://aclanthology.org/2022.sigdial-1.60
%U https://doi.org/10.18653/v1/2022.sigdial-1.60
%P 637-644
Markdown (Informal)
[N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models](https://aclanthology.org/2022.sigdial-1.60) (Sato et al., SIGDIAL 2022)
ACL