Akihiro Tamura


2022

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A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation
Kazuki Tani | Ryoya Yuasa | Kazuki Takikawa | Akihiro Tamura | Tomoyuki Kajiwara | Takashi Ninomiya | Tsuneo Kato
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents a new benchmark test dataset for multi-level complexity-controllable machine translation (MLCC-MT), which is MT controlling the complexity of the output at more than two levels. In previous research, MLCC-MT models have been evaluated on a test dataset automatically constructed from the Newsela corpus, which is a document-level comparable corpus with document-level complexity. The existing test dataset has the following three problems: (i) A source language sentence and its target language sentence are not necessarily an exact translation pair because they are automatically detected. (ii) A target language sentence and its simplified target language sentence are not necessarily exactly parallel because they are automatically aligned. (iii) A sentence-level complexity is not necessarily appropriate because it is transferred from an article-level complexity attached to the Newsela corpus. Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation. Moreover, we implement two MLCC-NMT frameworks with a Transformer architecture and report their performance on our test dataset as baselines for future research. Our test dataset and codes are released.

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Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data
Taiki Watanabe | Tomoya Ichikawa | Akihiro Tamura | Tomoya Iwakura | Chunpeng Ma | Tsuneo Kato
Proceedings of the 21st Workshop on Biomedical Language Processing

Named entity recognition (NER) is one of the elemental technologies, which has been used for knowledge extraction from biomedical text. As one of the NER improvement approaches, multi-task learning that learns a model from multiple training data has been used. Among multi-task learning, an auxiliary learning method, which uses an auxiliary task for improving its target task, has shown higher NER performance than conventional multi-task learning for improving all the tasks simultaneously by using only one auxiliary task in the auxiliary learning. We propose Multiple Utilization of NER Corpora Helpful for Auxiliary BLESsing (MUNCH ABLES). MUNCHABLES utilizes multiple training datasets as auxiliary training data by the following methods; the first one is to finetune the NER model of the target task by sequentially performing auxiliary learning for each auxiliary training dataset, and the other is to use all training datasets in one auxiliary learning. We evaluate MUNCHABLES on eight biomedical-related domain NER tasks, where seven training datasets are used as auxiliary training data. The experiment results show that MUNCHABLES achieves higher accuracy than conventional multi-task learning methods on average while showing state-of-the-art accuracy.

2021

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Grammatical Error Correction via Supervised Attention in the Vicinity of Errors
Hiromichi Ishii | Akihiro Tamura | Takashi Ninomiya
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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Synchronous Syntactic Attention for Transformer Neural Machine Translation
Hiroyuki Deguchi | Akihiro Tamura | Takashi Ninomiya
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, “Synchronous Syntactic Attention,” inspired by synchronous dependency grammars. The mechanism synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target-side self-attentions and the source-side self-attentions mapped by the encoder-decoder attention matrix. The experiments show that the proposed method improves the translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU).

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Utterance Position-Aware Dialogue Act Recognition
Yuki Yano | Akihiro Tamura | Takashi Ninomiya | Hiroaki Obayashi
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.

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Hie-BART: Document Summarization with Hierarchical BART
Kazuki Akiyama | Akihiro Tamura | Takashi Ninomiya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

This paper proposes a new abstractive document summarization model, hierarchical BART (Hie-BART), which captures hierarchical structures of a document (i.e., sentence-word structures) in the BART model. Although the existing BART model has achieved a state-of-the-art performance on document summarization tasks, the model does not have the interactions between sentence-level information and word-level information. In machine translation tasks, the performance of neural machine translation models has been improved by incorporating multi-granularity self-attention (MG-SA), which captures the relationships between words and phrases. Inspired by the previous work, the proposed Hie-BART model incorporates MG-SA into the encoder of the BART model for capturing sentence-word structures. Evaluations on the CNN/Daily Mail dataset show that the proposed Hie-BART model outperforms some strong baselines and improves the performance of a non-hierarchical BART model (+0.23 ROUGE-L).

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Contrastive Response Pairs for Automatic Evaluation of Non-task-oriented Neural Conversational Models
Koshiro Okano | Yu Suzuki | Masaya Kawamura | Tsuneo Kato | Akihiro Tamura | Jianming Wu
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Responses generated by neural conversational models (NCMs) for non-task-oriented systems are difficult to evaluate. We propose contrastive response pairs (CRPs) for automatically evaluating responses from non-task-oriented NCMs. We conducted an error analysis on responses generated by an encoder-decoder recurrent neural network (RNN) type NCM and created three types of CRPs corresponding to the three most frequent errors found in the analysis. Three NCMs of different response quality were objectively evaluated with the CRPs and compared to a subjective assessment. The correctness obtained by the three types of CRPs were consistent with the results of the subjective assessment.

2020

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A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking
Hideki Nakayama | Akihiro Tamura | Takashi Ninomiya
Proceedings of the Twelfth Language Resources and Evaluation Conference

Visually-grounded natural language processing has become an important research direction in the past few years. However, majorities of the available cross-modal resources (e.g., image-caption datasets) are built in English and cannot be directly utilized in multilingual or non-English scenarios. In this study, we present a novel multilingual multimodal corpus by extending the Flickr30k Entities image-caption dataset with Japanese translations, which we name Flickr30k Entities JP (F30kEnt-JP). To the best of our knowledge, this is the first multilingual image-caption dataset where the captions in the two languages are parallel and have the shared annotations of many-to-many phrase-to-region linking. We believe that phrase-to-region as well as phrase-to-phrase supervision can play a vital role in fine-grained grounding of language and vision, and will promote many tasks such as multilingual image captioning and multimodal machine translation. To verify our dataset, we performed phrase localization experiments in both languages and investigated the effectiveness of our Japanese annotations as well as multilingual learning realized by our dataset.

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Bilingual Subword Segmentation for Neural Machine Translation
Hiroyuki Deguchi | Masao Utiyama | Akihiro Tamura | Takashi Ninomiya | Eiichiro Sumita
Proceedings of the 28th International Conference on Computational Linguistics

This paper proposed a new subword segmentation method for neural machine translation, “Bilingual Subword Segmentation,” which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation. Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).

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Supervised Visual Attention for Multimodal Neural Machine Translation
Tetsuro Nishihara | Akihiro Tamura | Takashi Ninomiya | Yutaro Omote | Hideki Nakayama
Proceedings of the 28th International Conference on Computational Linguistics

This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image. The proposed visual attention mechanism captures the relationship between a word and an image region more precisely than a conventional visual attention mechanism trained through MNMT in an unsupervised manner. Our experiments on English-German and German-English translation tasks using the Multi30k dataset and on English-Japanese and Japanese-English translation tasks using the Flickr30k Entities JP dataset show that a Transformer-based MNMT model can be improved by incorporating our proposed supervised visual attention mechanism and that further improvements can be achieved by combining it with a supervised cross-lingual attention mechanism (up to +1.61 BLEU, +1.7 METEOR).

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Transformer-based Approach for Predicting Chemical Compound Structures
Yutaro Omote | Kyoumoto Matsushita | Tomoya Iwakura | Akihiro Tamura | Takashi Ninomiya
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

By predicting chemical compound structures from their names, we can better comprehend chemical compounds written in text and identify the same chemical compound given different notations for database creation. Previous methods have predicted the chemical compound structures from their names and represented them by Simplified Molecular Input Line Entry System (SMILES) strings. However, these methods mainly apply handcrafted rules, and cannot predict the structures of chemical compound names not covered by the rules. Instead of handcrafted rules, we propose Transformer-based models that predict SMILES strings from chemical compound names. We improve the conventional Transformer-based model by introducing two features: (1) a loss function that constrains the number of atoms of each element in the structure, and (2) a multi-task learning approach that predicts both SMILES strings and InChI strings (another string representation of chemical compound structures). In evaluation experiments, our methods achieved higher F-measures than previous rule-based approaches (Open Parser for Systematic IUPAC Nomenclature and two commercially used products), and the conventional Transformer-based model. We release the dataset used in this paper as a benchmark for the future research.

2019

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Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
Taiki Watanabe | Akihiro Tamura | Takashi Ninomiya | Takuya Makino | Tomoya Iwakura
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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. Our method en- ables the long short-term memory (LSTM) of the NER model to capture chemical com- pound paraphrases by sharing the parameters of the LSTM and character embeddings be- tween the two models. The experimental re- sults on the BioCreative IV’s CHEMDNER task show that our method improves chemi- cal NER and achieves state-of-the-art perfor- mance.

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Dependency-Based Self-Attention for Transformer NMT
Hiroyuki Deguchi | Akihiro Tamura | Takashi Ninomiya
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention. The dependency-based self-attention is trained to attend to the modifiee for each token under constraints based on the dependency relations, inspired by Linguistically-Informed Self-Attention (LISA). While LISA is originally proposed for Transformer encoder for semantic role labeling, this paper extends LISA to Transformer NMT by masking future information on words in the decoder-side dependency-based self-attention. Additionally, our dependency-based self-attention operates at sub-word units created by byte pair encoding. The experiments show that our model improves 1.0 BLEU points over the baseline model on the WAT’18 Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.

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Dependency-Based Relative Positional Encoding for Transformer NMT
Yutaro Omote | Akihiro Tamura | Takashi Ninomiya
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder’s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.

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Improving Neural Machine Translation with Neural Syntactic Distance
Chunpeng Ma | Akihiro Tamura | Masao Utiyama | Eiichiro Sumita | Tiejun Zhao
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)

The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En–Ch), and +1.0 (Ch–En) BLEU).

2018

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Forest-Based Neural Machine Translation
Chunpeng Ma | Akihiro Tamura | Masao Utiyama | Tiejun Zhao | Eiichiro Sumita
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.

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Neural Machine Translation Incorporating Named Entity
Arata Ugawa | Akihiro Tamura | Takashi Ninomiya | Hiroya Takamura | Manabu Okumura
Proceedings of the 27th International Conference on Computational Linguistics

This study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models’abilitytotranslatecompoundwordsseemschallengingbecausetheencoderreceivesaword, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore,the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.

2017

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CKY-based Convolutional Attention for Neural Machine Translation
Taiki Watanabe | Akihiro Tamura | Takashi Ninomiya
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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. The proposed attention represents every possible combination of source words (e.g., phrases and structures) through CNNs, which imitates the CKY table in the algorithm. NMT, incorporating the proposed attention, decodes a target sentence on the basis of the attention scores of the hidden states of CNNs. The proposed attention enables NMT to capture alignments from underlying structures of a source sentence without sentence parsing. The evaluations on the Asian Scientific Paper Excerpt Corpus (ASPEC) English-Japanese translation task show that the proposed attention gains 0.66 points in BLEU.

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Neural Machine Translation with Source Dependency Representation
Kehai Chen | Rui Wang | Masao Utiyama | Lemao Liu | Akihiro Tamura | Eiichiro Sumita | Tiejun Zhao
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.

2016

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Bilingual Segmented Topic Model
Akihiro Tamura | Eiichiro Sumita
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Unsupervised Word Alignment by Agreement Under ITG Constraint
Hidetaka Kamigaito | Akihiro Tamura | Hiroya Takamura | Manabu Okumura | Eiichiro Sumita
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Cross-lingual Text Classification Using Topic-Dependent Word Probabilities
Daniel Andrade | Kunihiko Sadamasa | Akihiro Tamura | Masaaki Tsuchida
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Recurrent Neural Networks for Word Alignment Model
Akihiro Tamura | Taro Watanabe | Eiichiro Sumita
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Distortion Model Considering Rich Context for Statistical Machine Translation
Isao Goto | Masao Utiyama | Eiichiro Sumita | Akihiro Tamura | Sadao Kurohashi
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Part-of-Speech Induction in Dependency Trees for Statistical Machine Translation
Akihiro Tamura | Taro Watanabe | Eiichiro Sumita | Hiroya Takamura | Manabu Okumura
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Bilingual Lexicon Extraction from Comparable Corpora Using Label Propagation
Akihiro Tamura | Taro Watanabe | Eiichiro Sumita
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Extractive Summarization Method for Contact Center Dialogues based on Call Logs
Akihiro Tamura | Kai Ishikawa | Masahiro Saikou | Masaaki Tsuchida
Proceedings of 5th International Joint Conference on Natural Language Processing

2007

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Japanese Dependency Analysis Using the Ancestor-Descendant Relation
Akihiro Tamura | Hiroya Takamura | Manabu Okumura
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2005

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Classification of Multiple-Sentence Questions
Akihiro Tamura | Hiroya Takamura | Manabu Okumura
Second International Joint Conference on Natural Language Processing: Full Papers