Summary of the paper

Title Speech Grammars for Textual Entailment Patterns in Multimodal Question Answering
Authors Daniel Sonntag and Bogdan Sacaleanu
Abstract Over the last several years, speech-based question answering (QA) has become very popular in contrast to pure search engine based approaches on a desktop. Open-domain QA systems are now much more powerful and precise, and they can be used in speech applications. Speech-based question answering systems often rely on predefined grammars for speech understanding. In order to improve the coverage of such complex AI systems, we reused speech patterns used to generate textual entailment patterns. These can make multimodal question understanding more robust. We exemplify this in the context of a domain-specific dialogue scenario. As a result, written text input components (e.g., in a textual input field) can deal with more flexible input according to the derived textual entailment patterns. A multimodal QA dialogue spanning over several domains of interest, i.e., personal address book entries, questions about the music domain and politicians and other celebrities, demonstrates how the textual input mode can be used in a multimodal dialogue shell.
Topics Question Answering, Semantic Web, Dialogue
Full paper Speech Grammars for Textual Entailment Patterns in Multimodal Question Answering
Slides Speech Grammars for Textual Entailment Patterns in Multimodal Question Answering
Bibtex @InProceedings{SONNTAG10.911,
  author = {Daniel Sonntag and Bogdan Sacaleanu},
  title = {Speech Grammars for Textual Entailment Patterns in Multimodal Question Answering},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
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