Search Results for author: Naoki Otani

Found 10 papers, 3 papers with code

Pre-tokenization of Multi-word Expressions in Cross-lingual Word Embeddings

no code implementations EMNLP 2020 Naoki Otani, Satoru Ozaki, Xingyuan Zhao, Yucen Li, Micaelah St Johns, Lori Levin

We propose a simple method for word translation of MWEs to and from English in ten languages: we first compile lists of MWEs in each language and then tokenize the MWEs as single tokens before training word embeddings.

Tokenization Translation +1

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection

no code implementations WS 2019 Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W. black

Irony detection is an important task with applications in identification of online abuse and harassment.

Toward Comprehensive Understanding of a Sentiment Based on Human Motives

1 code implementation ACL 2019 Naoki Otani, Eduard Hovy

In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions.

Transfer Learning

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Unsupervised Cross-lingual Transfer of Word Embedding Spaces

1 code implementation EMNLP 2018 Ruochen Xu, Yiming Yang, Naoki Otani, Yuexin Wu

Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages.

Bilingual Lexicon Induction Cross-Lingual Transfer +3

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