Abstract
When answering questions, major challenges are (a) to carefully determine the content of the answer and (b) phrase it in a proper way. In IMIX, we focus on two text-to-text generation techniques to accomplish this: content selection and sentence fusion. Using content selection, we can extend answers to an arbitrary length, providing not just a direct answer but also related information so to better address the user’s information need. In this process, we use a graph-based model to generate coherent answers. We then apply sentence fusion to combine partial answers from different sources into a single more complete answer, at the same time avoiding redundancy. The fusion process involves syntactic parsing, tree alignment and surface string generation.
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Bosma, W., Marsi, E., Krahmer, E., Theune, M. (2011). Text-to-Text Generation for Question Answering. In: van den Bosch, A., Bouma, G. (eds) Interactive Multi-modal Question-Answering. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17525-1_6
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DOI: https://doi.org/10.1007/978-3-642-17525-1_6
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