@inproceedings{hu-etal-2020-translation,
title = "Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling",
author = "Hu, Wenpeng and
Le, Ran and
Liu, Bing and
Ma, Jinwen and
Zhao, Dongyan and
Yan, Rui",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.363",
doi = "10.18653/v1/2020.coling-main.363",
pages = "4111--4122",
abstract = "Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks{--}machine translation and dialogue response generation{--}as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.",
}
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<abstract>Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks–machine translation and dialogue response generation–as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.</abstract>
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%0 Conference Proceedings
%T Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling
%A Hu, Wenpeng
%A Le, Ran
%A Liu, Bing
%A Ma, Jinwen
%A Zhao, Dongyan
%A Yan, Rui
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F hu-etal-2020-translation
%X Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks–machine translation and dialogue response generation–as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.
%R 10.18653/v1/2020.coling-main.363
%U https://aclanthology.org/2020.coling-main.363
%U https://doi.org/10.18653/v1/2020.coling-main.363
%P 4111-4122
Markdown (Informal)
[Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling](https://aclanthology.org/2020.coling-main.363) (Hu et al., COLING 2020)
ACL