Speech communication includes many important issues on natural language processing and they are related with desirable advanced speech translation systems. Advanced systems need to be able to handle the interaction for speech communication, pragmatics in speech, and arbitrariness of speech usage. General characteristics of speech communication are discussed. Also the various viewpoints regarding interaction, pragmatics, and arbitrary usage are discussed. Some of the present speech translation approaches are explained and new basic technologies are introduced. In this paper, a synthetic NLP technology such as a composite art form is proposed for speech communication and speech translation.

In our previous paper, we proposed a mixed category of words and parts-of-speech names the MWP category based on class N-gram modeling [1].However, we had not conrmed the eciency of MWP category.In this paper, we evaluate the proposed MWP category.At rst we use \coverage of words and category sequences to open data" and \perplexity to training data" for the evaluation and we conrmed the characteristics of parts-of-speech are useful to for generating a suitable class N-gram modeling.As a result of the speech recognition experimentation, we also conrmed that the class N-gram modeling using MWP category is effective in improving the recognition rate for open data that shows a low coverage of words and category sequences, without decreasing the recognition rate much for closed data.

EBMT (Example-Based Machine Translation) is proposed. EBMT retrieves similar examples (pairs of source phrases, sentences, or texts and their translations) from a database of examples, adapting the examples to translate a new input. EBMT has the following features: (1) It is easily upgraded simply by inputting appropriate examples to the database; (2) It assigns a reliability factor to the translation result; (3) It is accelerated effectively by both indexing and parallel computing; (4) It is robust because of best-match reasoning; and (5) It well utilizes translator expertise. A prototype system has been implemented to deal with a difficult translation problem for conventional Rule-Based Machine Translation (RBMT), i.e., translating Japanese noun phrases of the form "N1 no N2" into English. The system has achieved about a 78% success rate on average. This paper explains the basic idea of EBMT, illustrates the experiment in detail, explains the broad applicability of EBMT to several difficult translation problems for RBMT and discusses the advantages of integrating EBMT with RBMT.

This paper presents a unification-based approach to Japanese honorifics based on a version of HPSG (Head-driven Phrase Structure Grammar). Utterance parsing is based on lexical specifications of each lexical item, including honorifics, and a few general PSG rules using a parser capable of unifying cyclic feature structures. It is shown that the possible word orders of Japanese honorific predicate constituents can be automatically deduced in the proposed framework without independently specifying them. Discourse Information Change Rules (DICRs) that allow resolving a class of anaphors in honorific contexts are also formulated.

This paper present a new method for parsing English sentences. The parser called LUTE-EJ parser is combined with case analysis and ATNG-based analysis. LUTE-EJ parser has two interesting mechanical characteristics. One is providing a structured buffer, Structured Constituent Buffer, so as to hold previous fillers for a case structure, instead of case registers before a verb appears in a sentence. The other is extended HOLD mechanism(in ATN), in whose use an embedded clause, especially a "bedeleted" clause, is recursively analyzed by case analysis. This parser's features are (1)extracting a case filler, basically as a noun phrase, by ATNG-based analysis, including recursive case analysis, and (2)mixing syntactic and semantic analysis by using case frames in case analysis.