F. Jurčíček

Extension of HVS semantic parser by allowing left-right branching

The hidden vector state (HVS) parser is a popular method for semantic parsing. It is used in the language understanding module of the statistical based spoken dialog system. This paper presents an extension of the HVS semantic parser. It enables the parser to generate broader class of semantic trees. This modification can be used to improve the performance of the parser by generating not only the right-branching trees (like original HVS parser) but also limited left-branching trees and their combinations. The extension retains simplicity and properties of the original HVS parser. We tested the method on Czech human-human train timetable corpus. The modified HVS parser yields statistically significant improvement. The accuracy of the system increased from 50.4% to 58.3% absolutely.

Czech-Sign speech corpus for semantic based machine translation

This paper describes progress in a development of the human-human dialogue corpus for machine translation of spoken language. We have chosen a semantically annotated corpus of phone calls to a train timetable information center. The phone calls consist of inquiries regarding their train traveler plans. Corpus dialogue act tags incorporate abstract semantic meaning. We have enriched a part of the corpus with Sign Speech translation and we have proposed methods how to do automatic machine translation from Czech to Sign Speech using semantic annotation contained in the corpus.

Parameterization of the input in training the HVS semantic parser

The aim of this paper is to present an extension of the hidden vector state semantic parser. First, we describe the statistical semantic parsing and its decomposition into the semantic and the lexical model. Subsequently, we present the original hidden vector state parser. Then, we modify its lexical model so that it supports the use of the input sequence of feature vectors instead of the sequence of words. We compose the feature vector from the automatically generated linguistic features (lemma form and morphological tag of the original word). We also examine the effect of including the original word into the feature vector. Finally, we evaluate the modified semantic parser on the Czech Human-Human train timetable corpus. We found that the performance of the semantic parser improved significantly compared with the baseline hidden vector state parser.