Kazuki Ashihara


2019

pdf bib
Contextualized context2vec
Kazuki Ashihara | Tomoyuki Kajiwara | Yuki Arase | Satoru Uchida
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.

2018

pdf bib
Contextualized Word Representations for Multi-Sense Embedding
Kazuki Ashihara | Tomoyuki Kajiwara | Yuki Arase | Satoru Uchida
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation