@InProceedings{park-EtAl:2019:S19-2,
  author    = {Park, Cheoneum  and  Kim, Juae  and  Lee, Hyeon-gu  and  Amplayo, Reinald Kim  and  Kim, Harksoo  and  Seo, Jungyun  and  Lee, Changki},
  title     = {ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-2220}
}