Shonosuke Ishiwatari


Learning to Describe Unknown Phrases with Local and Global Contexts
Shonosuke Ishiwatari | Hiroaki Hayashi | Naoki Yoshinaga | Graham Neubig | Shoetsu Sato | Masashi Toyoda | Masaru Kitsuregawa
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.


A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size
Masato Neishi | Jin Sakuma | Satoshi Tohda | Shonosuke Ishiwatari | Naoki Yoshinaga | Masashi Toyoda
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

In this paper, we describe the team UT-IIS’s system and results for the WAT 2017 translation tasks. We further investigated several tricks including a novel technique for initializing embedding layers using only the parallel corpus, which increased the BLEU score by 1.28, found a practical large batch size of 256, and gained insights regarding hyperparameter settings. Ultimately, our system obtained a better result than the state-of-the-art system of WAT 2016. Our code is available on

Chunk-based Decoder for Neural Machine Translation
Shonosuke Ishiwatari | Jingtao Yao | Shujie Liu | Mu Li | Ming Zhou | Naoki Yoshinaga | Masaru Kitsuregawa | Weijia Jia
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT ‘16 English-to-Japanese translation task.


Accurate Cross-lingual Projection between Count-based Word Vectors by Exploiting Translatable Context Pairs
Shonosuke Ishiwatari | Nobuhiro Kaji | Naoki Yoshinaga | Masashi Toyoda | Masaru Kitsuregawa
Proceedings of the Nineteenth Conference on Computational Natural Language Learning