Masaaki Nagata


2021

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Context-aware Neural Machine Translation with Mini-batch Embedding
Makoto Morishita | Jun Suzuki | Tomoharu Iwata | Masaaki Nagata
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

It is crucial to provide an inter-sentence context in Neural Machine Translation (NMT) models for higher-quality translation. With the aim of using a simple approach to incorporate inter-sentence information, we propose mini-batch embedding (MBE) as a way to represent the features of sentences in a mini-batch. We construct a mini-batch by choosing sentences from the same document, and thus the MBE is expected to have contextual information across sentences. Here, we incorporate MBE in an NMT model, and our experiments show that the proposed method consistently outperforms the translation capabilities of strong baselines and improves writing style or terminology to fit the document’s context.

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Improving Neural RST Parsing Model with Silver Agreement Subtrees
Naoki Kobayashi | Tsutomu Hirao | Hidetaka Kamigaito | Manabu Okumura | Masaaki Nagata
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.

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Zero Pronouns Identification based on Span prediction
Sei Iwata | Taro Watanabe | Masaaki Nagata
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

The presence of zero-pronoun (ZP) greatly affects the downstream tasks of NLP in pro-drop languages such as Japanese and Chinese. To tackle the problem, the previous works identified ZPs as sequence labeling on the word sequence or the linearlized tree nodes of the input. We propose a novel approach to ZP identification by casting it as a query-based argument span prediction task. Given a predicate as a query, our model predicts the omission with ZP. In the experiments, our model surpassed the sequence labeling baseline.

2020

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SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP
Katsuki Chousa | Masaaki Nagata | Masaaki Nishino
Proceedings of the 28th International Conference on Computational Linguistics

We propose a novel method of automatic sentence alignment from noisy parallel documents. We first formalize the sentence alignment problem as the independent predictions of spans in the target document from sentences in the source document. We then introduce a total optimization method using integer linear programming to prevent span overlapping and obtain non-monotonic alignments. We implement cross-language span prediction by fine-tuning pre-trained multilingual language models based on BERT architecture and train them using pseudo-labeled data obtained from unsupervised sentence alignment method. While the baseline methods use sentence embeddings and assume monotonic alignment, our method can capture the token-to-token interaction between the tokens of source and target text and handle non-monotonic alignments. In sentence alignment experiments on English-Japanese, our method achieved 70.3 F1 scores, which are +8.0 points higher than the baseline method. In particular, our method improved by +53.9 F1 scores for extracting non-parallel sentences. Our method improved the downstream machine translation accuracy by 4.1 BLEU scores when the extracted bilingual sentences are used for fine-tuning a pre-trained Japanese-to-English translation model.

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JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus
Makoto Morishita | Jun Suzuki | Masaaki Nagata
Proceedings of the 12th Language Resources and Evaluation Conference

Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs can benefit from them. We constructed a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. We constructed the parallel corpus by broadly crawling the web and automatically aligning parallel sentences. Our collected corpus, called JParaCrawl, amassed over 8.7 million sentence pairs. We show how it includes a broader range of domains and how a neural machine translation model trained with it works as a good pre-trained model for fine-tuning specific domains. The pre-training and fine-tuning approaches achieved or surpassed performance comparable to model training from the initial state and reduced the training time. Additionally, we trained the model with an in-domain dataset and JParaCrawl to show how we achieved the best performance with them. JParaCrawl and the pre-trained models are freely available online for research purposes.

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A Test Set for Discourse Translation from Japanese to English
Masaaki Nagata | Makoto Morishita
Proceedings of the 12th Language Resources and Evaluation Conference

We made a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. For each discourse phenomenon, we systematically collected examples where the translation of the second sentence depends on the first sentence. Compared with a previous study on test sets for English-to-French discourse translation (CITATION), we needed different approaches to make the data because Japanese has zero pronouns and represents different senses in different characters. We improved the translation accuracy using context-aware neural machine translation, and the improvement mainly reflects the betterment of the translation of zero pronouns.

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Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
Kosuke Yamada | Tsutomu Hirao | Ryohei Sasano | Koichi Takeda | Masaaki Nagata
Findings of the Association for Computational Linguistics: EMNLP 2020

Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers’ information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.

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University of Tsukuba’s Machine Translation System for IWSLT20 Open Domain Translation Task
Hongyi Cui | Yizhen Wei | Shohei Iida | Takehito Utsuro | Masaaki Nagata
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we introduce University of Tsukuba’s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.

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Proceedings of the Fifth Conference on Machine Translation
Loïc Barrault | Ondřej Bojar | Fethi Bougares | Rajen Chatterjee | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Alexander Fraser | Yvette Graham | Paco Guzman | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Makoto Morishita | Christof Monz | Masaaki Nagata | Toshiaki Nakazawa | Matteo Negri
Proceedings of the Fifth Conference on Machine Translation

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Findings of the 2020 Conference on Machine Translation (WMT20)
Loïc Barrault | Magdalena Biesialska | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Matthias Huck | Eric Joanis | Tom Kocmi | Philipp Koehn | Chi-kiu Lo | Nikola Ljubešić | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

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A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT
Masaaki Nagata | Katsuki Chousa | Masaaki Nishino
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. Since this step is equivalent to a SQuAD v2.0 style question answering task, we solve it using the multilingual BERT, which is fine-tuned on manually created gold word alignment data. It is nontrivial to obtain accurate alignment from a set of independently predicted spans. We greatly improved the word alignment accuracy by adding to the question the source token’s context and symmetrizing two directional predictions. In experiments using five word alignment datasets from among Chinese, Japanese, German, Romanian, French, and English, we show that our proposed method significantly outperformed previous supervised and unsupervised word alignment methods without any bitexts for pretraining. For example, we achieved 86.7 F1 score for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised method.

2019

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Split or Merge: Which is Better for Unsupervised RST Parsing?
Naoki Kobayashi | Tsutomu Hirao | Kengo Nakamura | Hidetaka Kamigaito | Manabu Okumura | Masaaki Nagata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Rhetorical Structure Theory (RST) parsing is crucial for many downstream NLP tasks that require a discourse structure for a text. Most of the previous RST parsers have been based on supervised learning approaches. That is, they require an annotated corpus of sufficient size and quality, and heavily rely on the language and domain dependent corpus. In this paper, we present two language-independent unsupervised RST parsing methods based on dynamic programming. The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones. The second builds the optimal tree in terms of a similarity score function that is defined for merging two adjacent spans into a large one. Experimental results on English and German RST treebanks showed that our parser based on span merging achieved the best score, around 0.8 F1 score, which is close to the scores of the previous supervised parsers.

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Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints
Masaaki Nishino | Sho Takase | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, “Trims cash” is an anagram of “Christmas”. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.

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NTT Neural Machine Translation Systems at WAT 2019
Makoto Morishita | Jun Suzuki | Masaaki Nagata
Proceedings of the 6th Workshop on Asian Translation

In this paper, we describe our systems that were submitted to the translation shared tasks at WAT 2019. This year, we participated in two distinct types of subtasks, a scientific paper subtask and a timely disclosure subtask, where we only considered English-to-Japanese and Japanese-to-English translation directions. We submitted two systems (En-Ja and Ja-En) for the scientific paper subtask and two systems (Ja-En, texts, items) for the timely disclosure subtask. Three of our four systems obtained the best human evaluation performances. We also confirmed that our new additional web-crawled parallel corpus improves the performance in unconstrained settings.

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Mixed Multi-Head Self-Attention for Neural Machine Translation
Hongyi Cui | Shohei Iida | Po-Hsuan Hung | Takehito Utsuro | Masaaki Nagata
Proceedings of the 3rd Workshop on Neural Generation and Translation

Recently, the Transformer becomes a state-of-the-art architecture in the filed of neural machine translation (NMT). A key point of its high-performance is the multi-head self-attention which is supposed to allow the model to independently attend to information from different representation subspaces. However, there is no explicit mechanism to ensure that different attention heads indeed capture different features, and in practice, redundancy has occurred in multiple heads. In this paper, we argue that using the same global attention in multiple heads limits multi-head self-attention’s capacity for learning distinct features. In order to improve the expressiveness of multi-head self-attention, we propose a novel Mixed Multi-Head Self-Attention (MMA) which models not only global and local attention but also forward and backward attention in different attention heads. This enables the model to learn distinct representations explicitly among multiple heads. In our experiments on both WAT17 English-Japanese as well as IWSLT14 German-English translation task, we show that, without increasing the number of parameters, our models yield consistent and significant improvements (0.9 BLEU scores on average) over the strong Transformer baseline.

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Context-aware Neural Machine Translation with Coreference Information
Takumi Ohtani | Hidetaka Kamigaito | Masaaki Nagata | Manabu Okumura
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)

We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.

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NTT’s Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami | Makoto Morishita | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes NTT’s submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline. Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.

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Selecting Informative Context Sentence by Forced Back-Translation
Ryuichiro Kimura | Shohei Iida | Hongyi Cui | Po-Hsuan Hung | Takehito Utsuro | Masaaki Nagata
Proceedings of Machine Translation Summit XVII: Research Track

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A Multi-Hop Attention for RNN based Neural Machine Translation
Shohei Iida | Ryuichiro Kimura | Hongyi Cui | Po-Hsuan Hung | Takehito Utsuro | Masaaki Nagata
Proceedings of The 8th Workshop on Patent and Scientific Literature Translation

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Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
Kosuke Nishida | Kyosuke Nishida | Masaaki Nagata | Atsushi Otsuka | Itsumi Saito | Hisako Asano | Junji Tomita
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the system to return the answer with evidence sentences by reasoning and gathering disjoint pieces of the reference texts. It proposes the Query Focused Extractor (QFE) model for evidence extraction and uses multi-task learning with the QA model. QFE is inspired by extractive summarization models; compared with the existing method, which extracts each evidence sentence independently, it sequentially extracts evidence sentences by using an RNN with an attention mechanism on the question sentence. It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence. Experimental results show that QFE with a simple RC baseline model achieves a state-of-the-art evidence extraction score on HotpotQA. Although designed for RC, it also achieves a state-of-the-art evidence extraction score on FEVER, which is a recognizing textual entailment task on a large textual database.

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Attention over Heads: A Multi-Hop Attention for Neural Machine Translation
Shohei Iida | Ryuichiro Kimura | Hongyi Cui | Po-Hsuan Hung | Takehito Utsuro | Masaaki Nagata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In this paper, we propose a multi-hop attention for the Transformer. It refines the attention for an output symbol by integrating that of each head, and consists of two hops. The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer. The second hop attention is a combination of multi-layer perceptron (MLP) attention and head gate, which efficiently increases the complexity of the model by adding dependencies between heads. We demonstrate that the translation accuracy of the proposed multi-hop attention outperforms the baseline Transformer significantly, +0.85 BLEU point for the IWSLT-2017 German-to-English task and +2.58 BLEU point for the WMT-2017 German-to-English task. We also find that the number of parameters required for a multi-hop attention is smaller than that for stacking another self-attention layer and the proposed model converges significantly faster than the original Transformer.

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Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation
Go Yasui | Yoshimasa Tsuruoka | Masaaki Nagata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Traditional model training for sentence generation employs cross-entropy loss as the loss function. While cross-entropy loss has convenient properties for supervised learning, it is unable to evaluate sentences as a whole, and lacks flexibility. We present the approach of training the generation model using the estimated semantic similarity between the output and reference sentences to alleviate the problems faced by the training with cross-entropy loss. We use the BERT-based scorer fine-tuned to the Semantic Textual Similarity (STS) task for semantic similarity estimation, and train the model with the estimated scores through reinforcement learning (RL). Our experiments show that reinforcement learning with semantic similarity reward improves the BLEU scores from the baseline LSTM NMT model.

2018

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Improving Neural Machine Translation by Incorporating Hierarchical Subword Features
Makoto Morishita | Jun Suzuki | Masaaki Nagata
Proceedings of the 27th International Conference on Computational Linguistics

This paper focuses on subword-based Neural Machine Translation (NMT). We hypothesize that in the NMT model, the appropriate subword units for the following three modules (layers) can differ: (1) the encoder embedding layer, (2) the decoder embedding layer, and (3) the decoder output layer. We find the subword based on Sennrich et al. (2016) has a feature that a large vocabulary is a superset of a small vocabulary and modify the NMT model enables the incorporation of several different subword units in a single embedding layer. We refer these small subword features as hierarchical subword features. To empirically investigate our assumption, we compare the performance of several different subword units and hierarchical subword features for both the encoder and decoder embedding layers. We confirmed that incorporating hierarchical subword features in the encoder consistently improves BLEU scores on the IWSLT evaluation datasets.

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Reducing Odd Generation from Neural Headline Generation
Shun Kiyono | Sho Takase | Jun Suzuki | Naoaki Okazaki | Kentaro Inui | Masaaki Nagata
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Neural Tensor Networks with Diagonal Slice Matrices
Takahiro Ishihara | Katsuhiko Hayashi | Hitoshi Manabe | Masashi Shimbo | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. First, we evaluate our proposed NTN models on knowledge graph completion. Second, we extend the models to recursive NTNs (RNTNs) and evaluate them on logical reasoning tasks. These experiments show that our proposed models learn better and faster than the original (R)NTNs.

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Higher-Order Syntactic Attention Network for Longer Sentence Compression
Hidetaka Kamigaito | Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

A sentence compression method using LSTM can generate fluent compressed sentences. However, the performance of this method is significantly degraded when compressing longer sentences since it does not explicitly handle syntactic features. To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we trained HiSAN by maximizing jointly the probability of a correct output with the attention distribution. Experimental results on Google sentence compression dataset showed that our method achieved the best performance on F1 as well as ROUGE-1,2 and L scores, 83.2, 82.9, 75.8 and 82.7, respectively. In human evaluation, our methods also outperformed baseline methods in both readability and informativeness.

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Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
Shinsaku Sakaue | Tsutomu Hirao | Masaaki Nishino | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular objective functions, the efficiency of the greedy algorithm, and the provable performance guarantee. However, when it comes to compressive summarization, we are currently missing a counterpart of the extractive method based on submodularity. In this paper, we propose a fast greedy method for compressive summarization. Our method is applicable to any monotone submodular objective function, including many functions well-suited for document summarization. We provide an approximation guarantee of our greedy algorithm. Experiments show that our method is about 100 to 400 times faster than an existing method based on integer-linear-programming (ILP) formulations and that our method empirically achieves more than 95%-approximation.

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Pruning Basic Elements for Better Automatic Evaluation of Summaries
Ukyo Honda | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.

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An Empirical Study of Building a Strong Baseline for Constituency Parsing
Jun Suzuki | Sho Takase | Hidetaka Kamigaito | Makoto Morishita | Masaaki Nagata
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers’ performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.

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Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models
Shun Kiyono | Sho Takase | Jun Suzuki | Naoaki Okazaki | Kentaro Inui | Masaaki Nagata
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Developing a method for understanding the inner workings of black-box neural methods is an important research endeavor. Conventionally, many studies have used an attention matrix to interpret how Encoder-Decoder-based models translate a given source sentence to the corresponding target sentence. However, recent studies have empirically revealed that an attention matrix is not optimal for token-wise translation analyses. We propose a method that explicitly models the token-wise alignment between the source and target sequences to provide a better analysis. Experiments show that our method can acquire token-wise alignments that are superior to those of an attention mechanism.

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NTT’s Neural Machine Translation Systems for WMT 2018
Makoto Morishita | Jun Suzuki | Masaaki Nagata
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.

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Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries
Tsutomu Hirao | Hidetaka Kamigaito | Masaaki Nagata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper tackles automation of the pyramid method, a reliable manual evaluation framework. To construct a pyramid, we transform human-made reference summaries into extractive reference summaries that consist of Elementary Discourse Units (EDUs) obtained from source documents and then weight every EDU by counting the number of extractive reference summaries that contain the EDU. A summary is scored by the correspondences between EDUs in the summary and those in the pyramid. Experiments on DUC and TAC data sets show that our methods strongly correlate with various manual evaluations.

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Direct Output Connection for a High-Rank Language Model
Sho Takase | Jun Suzuki | Masaaki Nagata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also middle layers. This method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). Our proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to application tasks: machine translation and headline generation.

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.

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NTT Neural Machine Translation Systems at WAT 2017
Makoto Morishita | Jun Suzuki | Masaaki Nagata
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

In this year, we participated in four translation subtasks at WAT 2017. Our model structure is quite simple but we used it with well-tuned hyper-parameters, leading to a significant improvement compared to the previous state-of-the-art system. We also tried to make use of the unreliable part of the provided parallel corpus by back-translating and making a synthetic corpus. Our submitted system achieved the new state-of-the-art performance in terms of the BLEU score, as well as human evaluation.

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Hierarchical Word Structure-based Parsing: A Feasibility Study on UD-style Dependency Parsing in Japanese
Takaaki Tanaka | Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 15th International Conference on Parsing Technologies

In applying word-based dependency parsing such as Universal Dependencies (UD) to Japanese, the uncertainty of word segmentation emerges for defining a word unit of the dependencies. We introduce the following hierarchical word structures to dependency parsing in Japanese: morphological units (a short unit word, SUW) and syntactic units (a long unit word, LUW). An SUW can be used to segment a sentence consistently, while it is too short to represent syntactic construction. An LUW is a unit including functional multiwords and LUW-based analysis facilitates the capturing of syntactic structure and makes parsing results more precise than SUW-based analysis. This paper describes the results of a feasibility study on the ability and the effectiveness of parsing methods based on hierarchical word structure (LUW chunking+parsing) in comparison to single layer word structure (SUW parsing). We also show joint analysis of LUW-chunking and dependency parsing improves the performance of identifying predicate-argument structures, while there is not much difference between overall results of them. not much difference between overall results of them.

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Oracle Summaries of Compressive Summarization
Tsutomu Hirao | Masaaki Nishino | Masaaki Nagata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper derives an Integer Linear Programming (ILP) formulation to obtain an oracle summary of the compressive summarization paradigm in terms of ROUGE. The oracle summary is essential to reveal the upper bound performance of the paradigm. Experimental results on the DUC dataset showed that ROUGE scores of compressive oracles are significantly higher than those of extractive oracles and state-of-the-art summarization systems. These results reveal that compressive summarization is a promising paradigm and encourage us to continue with the research to produce informative summaries.

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Supervised Attention for Sequence-to-Sequence Constituency Parsing
Hidetaka Kamigaito | Katsuhiko Hayashi | Tsutomu Hirao | Hiroya Takamura | Manabu Okumura | Masaaki Nagata
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT). Recently, MT performances were improved by incorporating supervised attention into the model. In this paper, we introduce supervised attention to constituency parsing that can be regarded as another translation task. Evaluation results on the PTB corpus showed that the bracketing F-measure was improved by supervised attention.

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Input-to-Output Gate to Improve RNN Language Models
Sho Takase | Jun Suzuki | Masaaki Nagata
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.

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Enumeration of Extractive Oracle Summaries
Tsutomu Hirao | Masaaki Nishino | Jun Suzuki | Masaaki Nagata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.

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Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki | Masaaki Nagata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.

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K-best Iterative Viterbi Parsing
Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs). To achieve faster parsing, our proposal employs a pruning technique to reduce unnecessary edges in the search space. The key is to conduct repetitively Viterbi inside and outside parsing, while gradually expanding the search space to efficiently compute heuristic bounds used for pruning. Our experimental results using the English Penn Treebank corpus show that the proposed algorithm is faster than the standard CKY parsing algorithm. In addition, we also show how to extend this algorithm to extract k-best Viterbi parse trees.

2016

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Neural Headline Generation on Abstract Meaning Representation
Sho Takase | Jun Suzuki | Naoaki Okazaki | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Empty element recovery by spinal parser operations
Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Phrase Table Pruning via Submodular Function Maximization
Masaaki Nishino | Jun Suzuki | Masaaki Nagata
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Empirical comparison of dependency conversions for RST discourse trees
Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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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.

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Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016
Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2016 (Group ID: ntt) that uses syntactic pre-ordering over Chinese dependency structures. Chinese words are reordered by a learning-to-rank model based on pairwise classification to obtain word order close to Japanese. In this year’s system, two different machine translation methods are compared: traditional phrase-based statistical machine translation and recent sequence-to-sequence neural machine translation with an attention mechanism. Our pre-ordering showed a significant improvement over the phrase-based baseline, but, in contrast, it degraded the neural machine translation baseline.

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Right-truncatable Neural Word Embeddings
Jun Suzuki | Masaaki Nagata
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Exploring Text Links for Coherent Multi-Document Summarization
Xun Wang | Masaaki Nishino | Tsutomu Hirao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.

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

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Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015
Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Discriminative Preordering Meets Kendall’s 𝜏 Maximization
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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A Unified Learning Framework of Skip-Grams and Global Vectors
Jun Suzuki | Masaaki Nagata
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Word-based Japanese typed dependency parsing with grammatical function analysis
Takaaki Tanaka | Masaaki Nagata
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Hybrid Approach to PDTB-styled Discourse Parsing for CoNLL-2015
Yasuhisa Yoshida | Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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Empty Category Detection With Joint Context-Label Embeddings
Xun Wang | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization
Masaaki Nishino | Norihito Yasuda | Tsutomu Hirao | Shin-ichi Minato | Masaaki Nagata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Japanese-to-English patent translation system based on domain-adapted word segmentation and post-ordering
Katsuhito Sudoh | Masaaki Nagata | Shinsuke Mori | Tatsuya Kawahara
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

This paper presents a Japanese-to-English statistical machine translation system specialized for patent translation. Patents are practically useful technical documents, but their translation needs different efforts from general-purpose translation. There are two important problems in the Japanese-to-English patent translation: long distance reordering and lexical translation of many domain-specific terms. We integrated novel lexical translation of domain-specific terms with a syntax-based post-ordering framework that divides the machine translation problem into lexical translation and reordering explicitly for efficient syntax-based translation. The proposed lexical translation consists of a domain-adapted word segmentation and an unknown word transliteration. Experimental results show our system achieves better translation accuracy in BLEU and TER compared to the baseline methods.

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The NAIST-NTT TED talk treebank
Graham Neubig | Katsuhiro Sudoh | Yusuke Oda | Kevin Duh | Hajime Tsukuda | Masaaki Nagata
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Syntactic parsing is a fundamental natural language processing technology that has proven useful in machine translation, language modeling, sentence segmentation, and a number of other applications related to speech translation. However, there is a paucity of manually annotated syntactic parsing resources for speech, and particularly for the lecture speech that is the current target of the IWSLT translation campaign. In this work, we present a new manually annotated treebank of TED talks that we hope will prove useful for investigation into the interaction between syntax and these speechrelated applications. The first version of the corpus includes 1,217 sentences and 23,158 words manually annotated with parse trees, and aligned with translations in 26-43 different languages. In this paper we describe the collection of the corpus, and an analysis of its various characteristics.

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Dependency-based Discourse Parser for Single-Document Summarization
Yasuhisa Yoshida | Jun Suzuki | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Single Document Summarization based on Nested Tree Structure
Yuta Kikuchi | Tsutomu Hirao | Hiroya Takamura | Manabu Okumura | Masaaki Nagata
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Noise-Aware Character Alignment for Bootstrapping Statistical Machine Transliteration from Bilingual Corpora
Katsuhito Sudoh | Shinsuke Mori | Masaaki Nagata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Shift-Reduce Word Reordering for Machine Translation
Katsuhiko Hayashi | Katsuhito Sudoh | Hajime Tsukada | Jun Suzuki | Masaaki Nagata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Single-Document Summarization as a Tree Knapsack Problem
Tsutomu Hirao | Yasuhisa Yoshida | Masaaki Nishino | Norihito Yasuda | Masaaki Nagata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Two-Stage Pre-ordering for Japanese-to-English Statistical Machine Translation
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Effects of Parsing Errors on Pre-Reordering Performance for Chinese-to-Japanese SMT
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27)

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Using unlabeled dependency parsing for pre-reordering for Chinese-to-Japanese statistical machine translation
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Second Workshop on Hybrid Approaches to Translation

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Constructing a Practical Constituent Parser from a Japanese Treebank with Function Labels
Takaaki Tanaka | Masaaki Nagata
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages

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Supervised Model Learning with Feature Grouping based on a Discrete Constraint
Jun Suzuki | Masaaki Nagata
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Latent Semantic Matching: Application to Cross-language Text Categorization without Alignment Information
Tsutomu Hirao | Tomoharu Iwata | Masaaki Nagata
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Head Finalization Reordering for Chinese-to-Japanese Machine Translation
Dan Han | Katsuhito Sudoh | Xianchao Wu | Kevin Duh | Hajime Tsukada | Masaaki Nagata
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Zero Pronoun Resolution can Improve the Quality of J-E Translation
Hirotoshi Taira | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation

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The Effect of Learner Corpus Size in Grammatical Error Correction of ESL Writings
Tomoya Mizumoto | Yuta Hayashibe | Mamoru Komachi | Masaaki Nagata | Yuji Matsumoto
Proceedings of COLING 2012: Posters

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Learning to Translate with Multiple Objectives
Kevin Duh | Katsuhito Sudoh | Xianchao Wu | Hajime Tsukada | Masaaki Nagata
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing
Hiroyuki Shindo | Yusuke Miyao | Akinori Fujino | Masaaki Nagata
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Comparative Study of Target Dependency Structures for Statistical Machine Translation
Xianchao Wu | Katsuhito Sudoh | Kevin Duh | Hajime Tsukada | Masaaki Nagata
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Error Correcting Romaji-kana Conversion for Japanese Language Education
Seiji Kasahara | Mamoru Komachi | Masaaki Nagata | Yuji Matsumoto
Proceedings of the Workshop on Advances in Text Input Methods (WTIM 2011)

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Extracting Pre-ordering Rules from Predicate-Argument Structures
Xianchao Wu | Katsuhito Sudoh | Kevin Duh | Hajime Tsukada | Masaaki Nagata
Proceedings of 5th International Joint Conference on Natural Language Processing

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Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners
Tomoya Mizumoto | Mamoru Komachi | Masaaki Nagata | Yuji Matsumoto
Proceedings of 5th International Joint Conference on Natural Language Processing

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Distributed Minimum Error Rate Training of SMT using Particle Swarm Optimization
Jun Suzuki | Kevin Duh | Masaaki Nagata
Proceedings of 5th International Joint Conference on Natural Language Processing

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Generalized Minimum Bayes Risk System Combination
Kevin Duh | Katsuhito Sudoh | Xianchao Wu | Hajime Tsukada | Masaaki Nagata
Proceedings of 5th International Joint Conference on Natural Language Processing

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Extracting Pre-ordering Rules from Chunk-based Dependency Trees for Japanese-to-English Translation
Xianchao Wu | Katsuhito Sudoh | Kevin Duh | Hajime Tsukada | Masaaki Nagata
Proceedings of Machine Translation Summit XIII: Papers

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Post-ordering in Statistical Machine Translation
Katsuhito Sudoh | Xianchao Wu | Kevin Duh | Hajime Tsukada | Masaaki Nagata
Proceedings of Machine Translation Summit XIII: Papers

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Insertion Operator for Bayesian Tree Substitution Grammars
Hiroyuki Shindo | Akinori Fujino | Masaaki Nagata
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Is Machine Translation Ripe for Cross-Lingual Sentiment Classification?
Kevin Duh | Akinori Fujino | Masaaki Nagata
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Learning Condensed Feature Representations from Large Unsupervised Data Sets for Supervised Learning
Jun Suzuki | Hideki Isozaki | Masaaki Nagata
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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N-Best Reranking by Multitask Learning
Kevin Duh | Katsuhito Sudoh | Hajime Tsukada | Hideki Isozaki | Masaaki Nagata
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Divide and Translate: Improving Long Distance Reordering in Statistical Machine Translation
Katsuhito Sudoh | Kevin Duh | Hajime Tsukada | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Using Goi-Taikei as an Upper Ontology to Build a Large-Scale Japanese Ontology from Wikipedia
Masaaki Nagata | Yumi Shibaki | Kazuhide Yamamoto
Proceedings of the 6th Workshop on Ontologies and Lexical Resources

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Constructing Large-Scale Person Ontology from Wikipedia
Yumi Shibaki | Masaaki Nagata | Kazuhide Yamamoto
Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources

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Word Alignment with Synonym Regularization
Hiroyuki Shindo | Akinori Fujino | Masaaki Nagata
Proceedings of the ACL 2010 Conference Short Papers

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Predicate Argument Structure Analysis Using Transformation Based Learning
Hirotoshi Taira | Sanae Fujita | Masaaki Nagata
Proceedings of the ACL 2010 Conference Short Papers

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Enriching Dictionaries with Images from the Internet - Targeting Wikipedia and a Japanese Semantic Lexicon: Lexeed -
Sanae Fujita | Masaaki Nagata
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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BaseNP Supersense Tagging for Japanese Texts
Hirotoshi Taira | Sen Yoshida | Masaaki Nagata
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Utilizing Features of Verbs in Statistical Zero Pronoun Resolution for Japanese Speech
Sen Yoshida | Masaaki Nagata
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

2008

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A Japanese Predicate Argument Structure Analysis using Decision Lists
Hirotoshi Taira | Sanae Fujita | Masaaki Nagata
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2006

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A Clustered Global Phrase Reordering Model for Statistical Machine Translation
Masaaki Nagata | Kuniko Saito | Kazuhide Yamamoto | Kazuteru Ohashi
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Phrase reordering for statistical machine translation based on predicate-argument structure
Mamoru Komachi | Masaaki Nagata | Yuji Matsumoto
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

2005

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NUT-NTT Statistical Machine Translation System for IWSLT 2005
Kazuteru Ohashi | Kazuhide Yamamoto | Kuniko Saito | Masaaki Nagata
Proceedings of the Second International Workshop on Spoken Language Translation

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Proceedings of the ACL Interactive Poster and Demonstration Sessions
Masaaki Nagata | Ted Pedersen
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Portable Translator Capable of Recognizing Characters on Signboard and Menu Captured by its Built-in Camera
Hideharu Nakajima | Yoshihiro Matsuo | Masaaki Nagata | Kuniko Saito
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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Efficient Decoding for Statistical Machine Translation with a Fully Expanded WFST Model
Hajime Tsukada | Masaaki Nagata
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

2003

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Improving translation models by applying asymmetric learning
Setsuo Yamada | Masaaki Nagata | Kenji Yamada
Proceedings of Machine Translation Summit IX: Papers

The statistical Machine Translation Model has two components: a language model and a translation model. This paper describes how to improve the quality of the translation model by using the common word pairs extracted by two asymmetric learning approaches. One set of word pairs is extracted by Viterbi alignment using a translation model, the other set is extracted by Viterbi alignment using another translation model created by reversing the languages. The common word pairs are extracted as the same word pairs in the two sets of word pairs. We conducted experiments using English and Japanese. Our method improves the quality of a original translation model by 5.7%. The experiments also show that the proposed learning method improves the word alignment quality independent of the training domain and the translation model. Moreover, we show that common word pairs are almost as useful as regular dictionary entries for training purposes.

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Multi-Language Named-Entity Recognition System based on HMM
Kuniko Saito | Masaaki Nagata
Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition

2001

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Using the Web as a Bilingual Dictionary
Masaaki Nagata | Teruka Saito | Kenji Suzuki
Proceedings of the ACL 2001 Workshop on Data-Driven Methods in Machine Translation

2000

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Synchronous Morphological Analysis of Grapheme and Phoneme for Japanese OCR
Masaaki Nagata
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

1999

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A Part of Speech Estimation Method for Japanese Unknown Words using a Statistical Model of Morphology and Context
Masaaki Nagata
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Japanese OCR Error Correction using Character Shape Similarity and Statistical Language Model
Masaaki Nagata
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Japanese OCR Error Correction using Character Shape Similarity and Statistical Language Model
Masaaki Nagata
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

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A Self-Organizing Japanese Word Segmenter using Heuristic Word Identification and Re-estimation
Masaaki Nagata
Fifth Workshop on Very Large Corpora

1996

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Automatic Extraction of New Words from Japanese Texts using Generalized Forward-Backward Search
Masaaki Nagata
Conference on Empirical Methods in Natural Language Processing

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Context-Based Spelling Correction for Japanese OCR
Masaaki Nagata
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

1994

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A Stochastic Japanese Morphological Analyzer Using a Forward-DP Backward-A* N-Best Search Algorithm
Masaaki Nagata
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

1992

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An Empirical Study on Rule Granularity and Unification Interleaving Toward an Efficient Unification-Based Parsing System
Masaaki Nagata
COLING 1992 Volume 1: The 14th International Conference on Computational Linguistics

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A Spoken Language Translation System: SL-TRANS2
Tsuyoshi Morimoto | Masami Suzuki | Toshiyuki Takezawa | Gen’ichiro Kikui | Masaaki Nagata | Mutsuko Tomokiyo
COLING 1992 Volume 3: The 14th International Conference on Computational Linguistics

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