@inproceedings{cerisara-etal-2018-multi,
title = "Multi-task dialog act and sentiment recognition on Mastodon",
author = "Cerisara, Christophe and
Jafaritazehjani, Somayeh and
Oluokun, Adedayo and
Le, Hoa T.",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1063",
pages = "745--754",
abstract = "Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.",
}
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<abstract>Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.</abstract>
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%0 Conference Proceedings
%T Multi-task dialog act and sentiment recognition on Mastodon
%A Cerisara, Christophe
%A Jafaritazehjani, Somayeh
%A Oluokun, Adedayo
%A Le, Hoa T.
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F cerisara-etal-2018-multi
%X Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.
%U https://aclanthology.org/C18-1063
%P 745-754
Markdown (Informal)
[Multi-task dialog act and sentiment recognition on Mastodon](https://aclanthology.org/C18-1063) (Cerisara et al., COLING 2018)
ACL
- Christophe Cerisara, Somayeh Jafaritazehjani, Adedayo Oluokun, and Hoa T. Le. 2018. Multi-task dialog act and sentiment recognition on Mastodon. In Proceedings of the 27th International Conference on Computational Linguistics, pages 745–754, Santa Fe, New Mexico, USA. Association for Computational Linguistics.