Shunsuke Takeno


2017

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Controlling Target Features in Neural Machine Translation via Prefix Constraints
Shunsuke Takeno | Masaaki Nagata | Kazuhide Yamamoto
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

We propose prefix constraints, a novel method to enforce constraints on target sentences in neural machine translation. It places a sequence of special tokens at the beginning of target sentence (target prefix), while side constraints places a special token at the end of source sentence (source suffix). Prefix constraints can be predicted from source sentence jointly with target sentence, while side constraints (Sennrich et al., 2016) must be provided by the user or predicted by some other methods. In both methods, special tokens are designed to encode arbitrary features on target-side or metatextual information. We show that prefix constraints are more flexible than side constraints and can be used to control the behavior of neural machine translation, in terms of output length, bidirectional decoding, domain adaptation, and unaligned target word generation.

2016

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Integrating empty category detection into preordering Machine Translation
Shunsuke Takeno | Masaaki Nagata | Kazuhide Yamamoto
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

We propose a method for integrating Japanese empty category detection into the preordering process of Japanese-to-English statistical machine translation. First, we apply machine-learning-based empty category detection to estimate the position and the type of empty categories in the constituent tree of the source sentence. Then, we apply discriminative preordering to the augmented constituent tree in which empty categories are treated as if they are normal lexical symbols. We find that it is effective to filter empty categories based on the confidence of estimation. Our experiments show that, for the IWSLT dataset consisting of short travel conversations, the insertion of empty categories alone improves the BLEU score from 33.2 to 34.3 and the RIBES score from 76.3 to 78.7, which imply that reordering has improved For the KFTT dataset consisting of Wikipedia sentences, the proposed preordering method considering empty categories improves the BLEU score from 19.9 to 20.2 and the RIBES score from 66.2 to 66.3, which shows both translation and reordering have improved slightly.

2015

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Empty Category Detection using Path Features and Distributed Case Frames
Shunsuke Takeno | Masaaki Nagata | Kazuhide Yamamoto
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing