While the QWERTY keyboard is a standard text entry for Latin script languages on smart devices, it is not always true for non-Latin script languages. In Japanese, the most popular text entry on smartphones is a flick-based interface that systematically assigns more than fifty kana characters to twelve keys of a numeric keypad in combination with flick directions. Under these circumstances, studies on Japanese text entry on smartwatches have focused on an efficient interface design that takes advantage of the regularity of the kana consonant and vowel structure, but overlooked commonality with familiar interfaces. Thus, we propose PonDeFlick, a Japanese text entry that commonalizes the flick directions with the familiar smartphone interface while providing the entire touchscreen for gestural operation. A ten-day user study showed that PonDeFlick reached a text-entry speed of 57.7 characters per minute, significantly faster than the numeric-keypad-based interface and a modification of PonDeFlick without the commonality.

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.

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-taskoriented NCMs.We conducted an error analysis on responses generated by an encoderdecoder 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.

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).

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.

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.

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). 2019.

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.For statistical machine translation (SMT), forestbased 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-string NMT model).The BLEU score of the proposed method is higher than that of the string-to-string NMT, treebased NMT, and forest-based SMT systems.

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.

In statistical machine translation, translation prediction considers not only the aligned source word itself but also its source contextual information. Learning context representation is a promising method for improving translation results, particularly through neural networks. Most of the existing methods process context words sequentially and neglect source long-distance dependencies. In this paper, we propose a novel neural approach to source dependence-based context representation for translation prediction. The proposed model is capable of not only encoding source long-distance dependencies but also capturing functional similarities to better predict translations (i.e., word form translations and ambiguous word translations). To verify our method, the proposed mode is incorporated into phrase-based and hierarchical phrase-based translation models, respectively. Experiments on large-scale Chinese-to-English and English-to-German translation tasks show that the proposed approach achieves significant improvement over the baseline systems and outperforms several existing context-enhanced methods.

The sequence-to-sequence model is proven to be extremely successful in constituent parsing. It relies on one key technique, the probabilistic attention mechanism, to automatically select the context for prediction. Despite its successes, the probabilistic attention model does not always select the most important context. For example, the headword and boundary words of a subtree have been shown to be critical when predicting the constituent label of the subtree, but this contextual information becomes increasingly difficult to learn as the length of the sequence increases. In this study, we proposed a deterministic attention mechanism that deterministically selects the important context and is not affected by the sequence length. We implemented two different instances of this framework. When combined with a novel bottom-up linearization method, our parser demonstrated better performance than that achieved by the sequence-to-sequence parser with probabilistic attention mechanism.

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 attentional NMT with source dependency representation to improve translation performance of NMT, especially on 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.

This study proposes the bilingual segmented topic model (BiSTM), which hierarchically models documents by treating each document as a set of segments, e.g., sections.While previous bilingual topic models, such as bilingual latent Dirichlet allocation (BiLDA) (Mimno et al., 2009;Ni et al., 2009), consider only cross-lingual alignments between entire documents, the proposed model considers cross-lingual alignments between segments in addition to document-level alignments and assigns the same topic distribution to aligned segments.This study also presents a method for simultaneously inferring latent topics and segmentation boundaries, incorporating unsupervised topic segmentation (Du et al., 2013) into BiSTM.Experimental results show that the proposed model significantly outperforms BiLDA in terms of perplexity and demonstrates improved performance in translation pair extraction (up to +0.083 extraction accuracy).

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. 2015.

This study proposes a word alignment model based on a recurrent neural network (RNN), in which an unlimited alignment history is represented by recurrently connected hidden layers.We perform unsupervised learning using noise-contrastive estimation (Gutmann and Hyvärinen, 2010;Mnih and Teh, 2012), which utilizes artificially generated negative samples.Our alignment model is directional, similar to the generative IBM models (Brown et al., 1993).To overcome this limitation, we encourage agreement between the two directional models by introducing a penalty function that ensures word embedding consistency across two directional models during training.The RNN-based model outperforms the feed-forward neural network-based model (Yang et al., 2013) as well as the IBM Model 4 under Japanese-English and French-English word alignment tasks, and achieves comparable translation performance to those baselines for Japanese-English and Chinese-English translation tasks.

This article proposes a new distortion model for phrase-based statistical machine translation. In decoding, a distortion model estimates the source word position to be translated next (subsequent position; SP) given the last translated source word position (current position; CP). We propose a distortion model that can simultaneously consider the word at the CP, the word at an SP candidate, the context of the CP and an SP candidate, relative word order among the SP candidates, and the words between the CP and an SP candidate. These considered elements are called rich context . Our model considers rich context by discriminating label sequences that specify spans from the CP to each SP candidate. It enables our model to learn the effect of relative word order among SP candidates as well as to learn the effect of distances from the training data. In contrast to the learning strategy of existing methods, our learning strategy is that the model learns preference relations among SP candidates in each sentence of the training data. This leaning strategy enables consideration of all of the rich context simultaneously. In our experiments, our model had higher BLUE and RIBES scores for Japanese-English, Chinese-English, and German-English translation compared to the lexical reordering models.