Akiko Eriguchi


2019

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Combining Translation Memory with Neural Machine Translation
Akiko Eriguchi | Spencer Rarrick | Hitokazu Matsushita
Proceedings of the 6th Workshop on Asian Translation

In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system’s output.

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Incorporating Source-Side Phrase Structures into Neural Machine Translation
Akiko Eriguchi | Kazuma Hashimoto | Yoshimasa Tsuruoka
Computational Linguistics, Volume 45, Issue 2 - June 2019

Neural machine translation (NMT) has shown great success as a new alternative to the traditional Statistical Machine Translation model in multiple languages. Early NMT models are based on sequence-to-sequence learning that encodes a sequence of source words into a vector space and generates another sequence of target words from the vector. In those NMT models, sentences are simply treated as sequences of words without any internal structure. In this article, we focus on the role of the syntactic structure of source sentences and propose a novel end-to-end syntactic NMT model, which we call a tree-to-sequence NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our proposed model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. We have empirically compared the proposed model with sequence-to-sequence models in various settings on Chinese-to-Japanese and English-to-Japanese translation tasks. Our experimental results suggest that the use of syntactic structure can be beneficial when the training data set is small, but is not as effective as using a bi-directional encoder. As the size of training data set increases, the benefits of using a syntactic tree tends to diminish.

2017

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Learning to Parse and Translate Improves Neural Machine Translation
Akiko Eriguchi | Yoshimasa Tsuruoka | Kyunghyun Cho
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.

2016

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Tree-to-Sequence Attentional Neural Machine Translation
Akiko Eriguchi | Kazuma Hashimoto | Yoshimasa Tsuruoka
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation
Kazuma Hashimoto | Akiko Eriguchi | Yoshimasa Tsuruoka
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper describes our UT-KAY system that participated in the Workshop on Asian Translation 2016. Based on an Attention-based Neural Machine Translation (ANMT) model, we build our system by incorporating a domain adaptation method for multiple domains and an attention-based unknown word replacement method. In experiments, we verify that the attention-based unknown word replacement method is effective in improving translation scores in Chinese-to-Japanese machine translation. We further show results of manual analysis on the replaced unknown words.

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Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation
Akiko Eriguchi | Kazuma Hashimoto | Yoshimasa Tsuruoka
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT’16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experimenting on the English-to-Japanese translation task, we have confirmed that the character-based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word-based model.

2013

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High-quality Training Data Selection using Latent Topics for Graph-based Semi-supervised Learning
Akiko Eriguchi | Ichiro Kobayashi
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop