CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs

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Abstract

We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task. Our system comprises a set of stacked Bi-LSTMs trained on two complementary linguistic annotations. We explore the effectiveness of grammatical features (POS and clause type) through ablation. The reported experiments suggest that a multi-task learning approach using this external, grammatical knowledge is useful for detecting the extent of antecedents and consequents and performs nearly as well without the use of word embeddings.

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Sung, M. G., Bagherzadeh, P., & Bergler, S. (2020). CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 445–450). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.54

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