Japanese sentences have completely different word orders from corresponding English sentences. Typical phrase-based statistical machine translation (SMT) systems such as Moses search for the best word permutation within a given distance limit (distortion limit). For English-to-Japanese translation, we need a large distance limit to obtain acceptable translations, and the number of translation candidates is extremely large. Therefore, SMT systems often fail to find acceptable translations within a limited time. To solve this problem, some researchers use rule-based preprocessing approaches, which reorder English words just like Japanese by using dozens of rules. Our idea is based on the following two observations: (1) Japanese is a typical head-final language, and (2) we can detect heads of English sentences by a head-driven phrase structure grammar (HPSG) parser. The main contributions of this article are twofold: First, we demonstrate how off-the-shelf, state-of-the-art HPSG parser enables us to write the reordering rules in an abstract level and can easily improve the quality of English-to-Japanese translation. Second, we also show that syntactic heads achieve better results than semantic heads. The proposed method outperforms the best system of NTCIR-7 PATMT EJ task.

To build trust or cultivate long-term relationships with users, conversational systems need to perform social dialogue. To date, research has primarily focused on the overall effect of social dialogue in human-computer interaction, leading to little work on the effects of individual linguistic phenomena within social dialogue. This paper investigates such individual effects through dialogue experiments. Focusing on self-disclosure and empathic utterances (agreement and disagreement), we empirically calculate their contributions to the dialogue quality. Our analysis shows that (1) empathic utterances by users are strong indicators of increasing closeness and user satisfaction, (2) the system's empathic utterances are effective for inducing empathy from users, and (3) self-disclosure by users increases when users have positive preferences on topics being discussed.

This article describes our approach for answering why-questions that we initially introduced at NTCIR-6 QAC-4. The approach automatically acquires causal expression patterns from relation-annotated corpora by abstracting text spans annotated with a causal relation and by mining syntactic patterns that are useful for distinguishing sentences annotated with a causal relation from those annotated with other relations. We use these automatically acquired causal expression patterns to create features to represent answer candidates, and use these features together with other possible features related to causality to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms baselines with a Mean Reciprocal Rank (top-5) of 0.223 when sentences are used as answers and with a MRR (top-5) of 0.326 when paragraphs are used as answers, making it presumably the best-performing fully implemented why-QA system. Experimental results also verified the usefulness of the automatically acquired causal expression patterns.

Our new research project called concentrates on the creation of new lifestyles through research on communication science and intelligence integration. It is premised on the creation of such virtual communication partners as fairies and goblins that can be constantly at our side. We call these virtual communication partners mushrooms.To show the essence of ambient intelligence, we developed two multimodal prototype systems: mushrooms that watch, listen, and answer questions and a Quizmaster Mushroom. These two systems work in real time using speech, sound, dialogue, and vision technologies.We performed preliminary experiments with the Quizmaster Mushroom. The results showed that the system can transmit knowledge to users while they are playing the quizzes.Furthermore, through the two mushrooms, we found policies for design effects in multimodal interface and integration.

This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keeptheir attention on receiving information, it is expectedthat information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding. Index Terms: information presentation, quiz, user understanding

This paper proposes a named entity recognition (NER) method for speech recognition results that uses confidence on automatic speech recognition (ASR) as a feature.The ASR confidence feature indicates whether each word has been correctly recognized.The NER model is trained using ASR results with named entity (NE) labels as well as the corresponding transcriptions with NE labels.In experiments using support vector machines (SVMs) and speech data from Japanese newspaper articles, the proposed method outperformed a simple application of textbased NER to ASR results in NER Fmeasure by improving precision.These results show that the proposed method is effective in NER for noisy inputs.

This paper proposes a framework for training Conditional Random Fields (CRFs) to optimize multivariate evaluation measures, including non-linear measures such as F-score. Our proposed framework is derived from an error minimization approach that provides a simple solution for directly optimizing any evaluation measure. Specifically focusing on sequential segmentation tasks, i.e. text chunking and named entity recognition, we introduce a loss function that closely reflects the target evaluation measure for these tasks, namely, segmentation F-score. Our experiments show that our method performs better than standard CRF training.

We present a hierarchical phrase-based statistical machine translation in which a target sentence is efficiently generated in left-to-right order. The model is a class of synchronous-CFG with a Greibach Normal Form-like structure for the projected production rule: The paired target-side of a production rule takes a phrase prefixed form. The decoder for the target-normalized form is based on an Early-style top down parser on the source side. The target-normalized form coupled with our top down parser implies a left-to-right generation of translations which enables us a straightforward integration with ngram language models. Our model was experimented on a Japanese-to-English newswire translation task, and showed statistically significant performance improvements against a phrase-based translation system.

This paper introduces a new application of boosting for parse reranking.Several parsers have been proposed that utilize the all-subtrees representation (e.g., tree kernel and data oriented parsing).This paper argues that such an all-subtrees representation is extremely redundant and a comparable accuracy can be achieved using just a small set of subtrees.We show how the boosting algorithm can be applied to the all-subtrees representation and how it selects a small and relevant feature set efficiently.Two experiments on parse reranking show that our method achieves comparable or even better performance than kernel methods and also improves the testing efficiency.

This paper proposes a new approach to feature selection based on a sta- tistical feature mining technique for sequence and tree kernels. Since natural language data take discrete structures, convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing tasks. However, experi- ments have shown that the best results can only be achieved when lim- ited small sub-structures are dealt with by these kernels. This paper dis- cusses this issue of convolution kernels and then proposes a statistical feature selection that enable us to use larger sub-structures effectively. The proposed method, in order to execute efficiently, can be embedded into an original kernel calculation process by using sub-structure min- ing algorithms. Experiments on real NLP tasks confirm the problem in the conventional method and compare the performance of a conventional method to that of the proposed method.

Twenty-five Japanese Question Answering systems participated in NTCIR QAC2 subtask 1. Of these, our system SAIQA-QAC2 performed the best: MRR = 0.607. SAIQA-QAC2 is an improvement on our previous system SAIQA-Ii that achieved MRR = 0.46 for QAC1. We mainly improved the answer-type determination module and the retrieval module. In general, a fine-grained answer taxonomy improves QA performance but it is difficult to build an accurate answer extraction module for the fine-grained taxonomy because Machine Learning methods require a huge training corpus and hand-crafted rules are hard to maintain. Therefore, we built a fine-grained system by using a coarse-grained named entity recognizer and a Japanese lexicon “Nihongo Goi-taikei.” Our experiments show that named entity/numerical expression recognition and word sense-based answer extraction mainly contributed to the performance. In addition, we developed a new proximity-based document retrieval module that performs better than BM25. We also compared its performance with MultiText, a conventional proximity-based retrieval method developed for QA.

Convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing (NLP) tasks. Experiments have, however, shown that the over-fitting problem often arises when these kernels are used in NLP tasks. This paper discusses this issue of convolution kernels, and then proposes a new approach based on statistical feature selection that avoids this issue. To enable the proposed method to be executed efficiently, it is embedded into an original kernel calculation process by using sub-structure mining algorithms. Experiments are undertaken on real NLP tasks to confirm the problem with a conventional method and to compare its performance with that of the proposed method.

Recently, open-domain question answering (ODQA) systems that extract an exact answer from large text corpora based on text input are intensively being investigated. However, the information in the first question input by a user is not usually enough to yield the desired answer. Interactions for collecting additional information to accomplish QA is needed. This paper proposes an interactive approach for spoken interactive ODQA systems. When the reliabilities for answer hypotheses obtained by an ODQA system are low, the system automatically derives disambiguous queries (DQ) that draw out additional information. The additional information based on the DQ should contribute to distinguishing effectively an exact answer and to supplementing a lack of information by recognition errors. In our spoken interactive ODQA system, SPIQA, spoken questions are recognized by an ASR system, and DQ are automatically generated to disambiguate the transcribed questions. We confirmed the appropriateness of the derived DQ by comparing them with manually prepared ones.

Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, location, and date. NE recognition plays an essential role in information extraction systems and question answering systems. It is well known that hand-crafted systems with a large set of heuristic rules are difficult to maintain, and corpus-based statistical approaches are expected to be more robust and require less human intervention. Several statistical approaches have been reported in the literature. In a recent Japanese NE workshop, a maximum entropy (ME) system outperformed decision tree systems and most hand-crafted systems. Here, we propose an alternative method based on a simple rule generator and decision tree learning. Our experiments show that its performance is comparable to the ME approach. We also found that it can be trained more efficiently with a large set of training data and that it improves readability.