arxiv:1805.05370

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

Published on May 14, 2018

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Abstract

The paper presents a model that uses an entity library to enhance character identification in multiparty dialogues, improving the handling of infrequent characters.

AI-generated summary

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 effective learning from sparse or unbalanced data.

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