Papers by Satoru Uchida

5 papers
Controllable Text Simplification with Deep Reinforcement Learning (2022.aacl-short)

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Challenge: Existing methods for controlling sentence difficulty have not taken into account sentence-level difficulties.
Approach: They propose a method for controlling the difficulty of a sentence based on deep reinforcement learning.
Outcome: The proposed method generates sentences of appropriate difficulty for the target audience through reinforcement learning.
Contextualized context2vec (D19-55)

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Challenge: Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence.
Approach: They propose a method that combines two approaches to contextualize word embeddings for lexical substitution.
Outcome: The proposed method outperforms the current state-of-the-art method and assigns English proficiency levels to all target words and substitution candidates.
CEFR-based Lexical Simplification Dataset (L18-1)

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Challenge: Existing tools for lexical simplification are not tailored to language education with word levels and lists of candidates subjective.
Approach: They construct a language dataset for lexical simplification based on CEFR levels . target and candidate words are assigned CEFR-J wordlists and English Vocabulary Profile .
Outcome: The proposed method is based on the common European Framework of References for Languages (CEFR) levels and candidates are selected using an online thesaurus.
Distractor Generation for Fill-in-the-Blank Exercises by Question Type (2023.acl-srw)

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Challenge: Existing studies have generated words that are semantically similar to the correct words as distractors for fill-in-the-blank questions.
Approach: They propose a method to automatically generate distractors for fill-in-the-blank questions in entrance examinations for Japanese universities.
Outcome: The proposed method is effective on 500 actual questions on English fill-in-the-blank questions in Japanese universities.
CEFR-Based Sentence Difficulty Annotation and Assessment (2022.emnlp-main)

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Challenge: Controllable text simplification is a crucial assistive technique for language learning and teaching.
Approach: They propose a sentence-level assessment model to handle unbalanced level distribution . previous studies have suggested that controllable text simplification is difficult to apply .
Outcome: The proposed method outperforms baselines in readability assessment by scoring macro-F1 on the level assessment.

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