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.

This paper deals with the constrained DAG shortest path problem (CDSP), which finds the shortest path on a given directed acyclic graph (DAG) under any logical constraints posed on taken edges. There exists a previous work that uses binary decision diagrams (BDDs) to represent the logical constraints, and traverses the input DAG and the BDD simultaneously. The time and space complexity of this BDD-based method is derived from BDD size, and tends to be fast only when BDDs are small. However, since it does not prioritize the search order, there is considerable room for improvement, particularly for large BDDs. We combine the well-known A* search with the BDD-based method synergistically, and implement several novel heuristic functions. The key insight here is that the ‘shortest path’ in the BDD is a solution of a relaxed problem, just as the shortest path in the DAG is. Experiments, particularly practical machine learning applications, show that the proposed method decreases search time by up to 2 orders of magnitude, with the specific result that it is 2,000 times faster than a commercial solver. Moreover, the proposed method can reduce the peak memory usage up to 40 times less than the conventional method.