@inproceedings{park-etal-2019-thisiscompetition,
title = "{T}his{I}s{C}ompetition at {S}em{E}val-2019 Task 9: {BERT} is unstable for out-of-domain samples",
author = "Park, Cheoneum and
Kim, Juae and
Lee, Hyeon-gu and
Amplayo, Reinald Kim and
Kim, Harksoo and
Seo, Jungyun and
Lee, Changki",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2220",
doi = "10.18653/v1/S19-2220",
pages = "1254--1261",
abstract = "This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78{\%} F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59{\%} on Subtask B (i.e. out-of-domain), even without using any additional external data.",
}
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<abstract>This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78% F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59% on Subtask B (i.e. out-of-domain), even without using any additional external data.</abstract>
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%0 Conference Proceedings
%T ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
%A Park, Cheoneum
%A Kim, Juae
%A Lee, Hyeon-gu
%A Amplayo, Reinald Kim
%A Kim, Harksoo
%A Seo, Jungyun
%A Lee, Changki
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F park-etal-2019-thisiscompetition
%X This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78% F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59% on Subtask B (i.e. out-of-domain), even without using any additional external data.
%R 10.18653/v1/S19-2220
%U https://aclanthology.org/S19-2220
%U https://doi.org/10.18653/v1/S19-2220
%P 1254-1261
Markdown (Informal)
[ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples](https://aclanthology.org/S19-2220) (Park et al., SemEval 2019)
ACL
- Cheoneum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, and Changki Lee. 2019. ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1254–1261, Minneapolis, Minnesota, USA. Association for Computational Linguistics.