Papers by Kimitaka Asatani

1 papers
Lexical Entailment with Hierarchy Representations by Deep Metric Learning (2022.findings-emnlp)

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Challenge: Existing lexical entailment studies cannot be applied to words that are not included in the training dataset.
Approach: They propose a method that learns a mapping from word embeddings to hierarchical embedds to predict hypernymy relations among words.
Outcome: The proposed method achieves state-of-the-art performance and robustness for unknown words.

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