AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library

Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera, Gemma Boleda


Abstract
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an entity library. Our results show that this innovation greatly facilitates the identification of infrequent characters. Because of the generic nature of our model, this finding is potentially relevant to any task that requires the effective learning from sparse or imbalanced data.
Anthology ID:
S18-1008
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–69
Language:
URL:
https://aclanthology.org/S18-1008
DOI:
10.18653/v1/S18-1008
Bibkey:
Cite (ACL):
Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera, and Gemma Boleda. 2018. AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 65–69, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library (Aina et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1008.pdf
Code
 amore-upf/semeval2018-task4