Toshiyuki Sekiya


Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
Dong-Ho Lee | Akshen Kadakia | Kangmin Tan | Mahak Agarwal | Xinyu Feng | Takashi Shibuya | Ryosuke Mitani | Toshiyuki Sekiya | Jay Pujara | Xiang Ren
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model’s performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.


m_y at SemEval-2019 Task 9: Exploring BERT for Suggestion Mining
Masahiro Yamamoto | Toshiyuki Sekiya
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents our system to the SemEval-2019 Task 9, Suggestion Mining from Online Reviews and Forums. The goal of this task is to extract suggestions such as the expressions of tips, advice, and recommendations. We explore Bidirectional Encoder Representations from Transformers (BERT) focusing on target domain pre-training in Subtask A which provides training and test datasets in the same domain. In Subtask B, the cross domain suggestion mining task, we apply the idea of distant supervision. Our system obtained the third place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy of our approaches.