Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data

no code implementations • BioNLP (ACL) 2022 • Taiki Watanabe, Tomoya Ichikawa, Akihiro Tamura, Tomoya Iwakura, Chunpeng Ma, Tsuneo Kato

As one of the NER improvement approaches, multi-task learning that learns a model from multiple training data has been used.

Auxiliary Learning Multi-Task Learning +3

Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain

no code implementations • RANLP 2021 • Satoshi Hiai, Kazutaka Shimada, Taiki Watanabe, Akiva Miura, Tomoya Iwakura

In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.

Relation Relation Extraction

Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing

no code implementations • IJCNLP 2019 • Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, Tomoya Iwakura

We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model.

Multi-Task Learning named-entity-recognition +2

CKY-based Convolutional Attention for Neural Machine Translation

no code implementations • IJCNLP 2017 • Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya

This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm.

Machine Translation NMT +2