Hajime Tsukada

Left-to-Right Target Generation for Hierarchical Phrase-Based Translation

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.

Incorporating Speech Recognition Confidence into Discriminative Named Entity Recognition of Speech Data

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 text-based NER to ASR results in NER F-measure by improving precision. These results show that the proposed method is effective in NER for noisy inputs.

Spoken Interactive ODQA System: SPIQA

We have been investigating an interactive approach for Open-domain QA (ODQA) and have constructed a spoken interactive ODQA system, SPIQA. The system derives disambiguating queries (DQs) that draw out additional information. To test the efficiency of additional information requested by the DQs, the system reconstructs the users initial question by combining the addition information with question. The combination is then used for answer extraction. Experimental results revealed the potential of the generated DQs.

HPSG-Based Preprocessing for English-to-Japanese Translation

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.

Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation

This article proposes a novel reordering method for efficient two-step Japanese-to-English statistical machine translation (SMT) that isolates reordering from SMT and solves it after lexical translation. This reordering problem, called post-ordering, is solved as an SMT problem from Head-Final English (HFE) to English. HFE is syntax-based reordered English that is very successfully used for reordering with English-to-Japanese SMT. The proposed method incorporates its advantage into the reverse direction, Japanese-to-English, and solves the post-ordering problem by accurate syntax-based SMT with target language syntax. Two-step SMT with the proposed post-ordering empirically reduces the decoding time of the accurate but slow syntax-based SMT by its good approximation using intermediate HFE. The proposed method improves the decoding speed of syntax-based SMT decoding by about six times with comparable translation accuracy in Japanese-to-English patent translation experiments.

Instance-based generation for interactive restricted domain question answering systems

One important component of interactive systems is the generation component. While template-based generation is appropriate in many cases (for example, task oriented spoken dialogue systems), interactive question answering systems require a more sophisticated approach. In this paper, we propose and compare two example-based methods for generation of information seeking questions.

Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence

This paper proposes a discriminative named entity recognition (NER) method from automatic speech recognition (ASR) results. The proposed method uses the confidence of the ASR result as a feature that represents whether each word has been correctly recognized. Consequently, it provides robust NER for the noisy input caused by ASR errors. The NER model is trained using ASR results and reference transcriptions with named entity (NE) annotation. Experimental results using support vector machines (SVMs) and speech data from Japanese newspaper articles show that the proposed method outperformed a simple application of text-based NER to the ASR results, especially in terms of improving precision.