Search Results for author: Satoshi Sekine

Found 21 papers, 5 papers with code

Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set

no code implementations LREC 2020 Hassan S. Shavarani, Satoshi Sekine

Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions.

Classification General Classification

Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring

no code implementations WS 2019 Tomoya Mizumoto, Hiroki Ouchi, Yoriko Isobe, Paul Reisert, Ryo Nagata, Satoshi Sekine, Kentaro Inui

This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.

Can neural networks understand monotonicity reasoning?

1 code implementation WS 2019 Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.

Data Augmentation Natural Language Inference

What Makes Reading Comprehension Questions Easier?

1 code implementation EMNLP 2018 Saku Sugawara, Kentaro Inui, Satoshi Sekine, Akiko Aizawa

From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions.

Machine Reading Comprehension

An Empirical Study on Fine-Grained Named Entity Recognition

no code implementations COLING 2018 Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine

However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages.

Chatbot Named Entity Recognition +1

An Entity-Based approach to Answering Recurrent and Non-Recurrent Questions with Past Answers

no code implementations WS 2016 Anietie Andy, Mugizi Rwebangira, Satoshi Sekine

For unanswered questions that do not have a past resolved question with a shared need, we propose to use the best answer to a past resolved question with similar needs.

Community Question Answering Entity Linking

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