2019 Scholars at Work Conference Program

Program for the 2019 Scholars at Work Conference at Minnesota State University, Mankato on March 29, 2019

Domain Action Classification and Argument Parsing for Interlingua-Based Spoken Language Translation

Interlingua-based machine translation systems divide the machine translation process into two phases: analysis and generation. Source language input is converted into an interlingua representation during the analysis phase, and target language output is produced from the interlingua representation during the generation phase. For spoken language translation, the analysis phase typically includes an automatic speech recognizer that transforms a source language acoustic signal into text and an analyzer that produces interlingua representations for the source language text. We describ

Parsing Domain Actions With Phrase-Level Grammars And Memory-Based Learners

In this paper, we describe an approach to analysis for spoken language translation that combines phrase-level grammar-based parsing and automatic domain action classification. The job of the analyzer is to transform utterances into a shallow semantic task-oriented interlingua representation. The goal of our hybrid approach is to provide accurate real-time analyses and to improve robustness and portability to new domains and languages

Domain Specific Speech Acts for Spoken Language Translation

We describe a coding scheme for machine translation of spoken taskoriented dialogue. The coding scheme covers two levels of speaker intention - domain independent speech acts and domain dependent domain actions. Our database contains over 14,000 tagged sentences in English, Italian, and German. We argu

Spoken Language Parsing Using Phrase-Level Grammars and Trainable Classifiers

In this paper, we describe a novel approach to spoken language analysis for translation, which uses a combination of grammar-based phrase-level parsing and automatic classification. The job of the analyzer is to produce a shallow semantic interlingua representation for spoken task-oriented utterances. The goal of our hybrid approach is to provide accurate real-time analyses while improving robustness and portability to new domains and languages