A Unification-Based Japanese Parser for Speech-to-Speech Translation

Masaaki NAGATA  Tsuyoshi MORIMOTO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.1   pp.51-61
Publication Date: 1993/01/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Speech and Discourse Processing in Dialogue Systems)
Category: 
Keyword: 
unification,  japanese,  parser,  speech-to-speech translation,  

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Summary: 
A unification-based Japanese parser has been implemented for an experimental Japanese-to-English spoken language translation system (SL-TRANS). The parser consists of a unification-based spoken-style Japanese grammar and an active chart parser. The grammar handles the syntactic, semantic, and pragmatic constraints in an integrated fashion using HPSG-based framework in order to cope with speech recognition errors. The parser takes multiple sentential candidates from the HMM-LR speech recognizer, and produces a semantic representation associated with the best scoring parse based on acoustic and linguistic plausibility. The unification-based parser has been tested using 12 dialogues in the conference registration domain, which include 261 sentences uttered by one male speaker. The sentence recognition accuracy of the underlying speech recognizer is 73.6% for the top candidate, and 83.5% for the top three candidates, where the test-set perplexity of the CFG grammar is 65. By ruling out erroneous speech recognition results using various linguistic constraints, the parser improves the sentence recognition accuracy up to 81.6% for the top candidate, and 85.8% for the top three candidates. From the experiment result, we found that the combination of syntactic restriction, selectional restriction and coordinate structure restriction can provide a sufficient restriction to rule out the recognition errors between case-marking particles with the same vowel, which are the type of errors most likely to occur. However, we also found that it is necessary to use pragmatic information, such as topic, presupposition, and discourse structure, to rule out the recognition errors involved with topicalizing particles and sentence final particles.