KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem

Abstract

Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder-decoder model. The input document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder-decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder-decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00% and a label accuracy of 85.10% at the scene-level.

What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue

“…Table 1 gives our results in the two evaluations, comparing the models described in Section 3 to the best performing models in the SemEval 2018 Task 4 competition (Aina et al, 2018;Park et al, 2018). Recall that our goal in this paper is not to optimize performance, but to understand model behavior; however, results show that these models are worth analyzing, as that they outperform the state of the art.…”