Abstract
An essential task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question. Previous neural models have experimented with using additional text together with the target sentence to learn a selection function but these methods were not powerful enough to effectively encode contextual information. In this paper, we analyze the role of contextual information for the sentence selection task in Transformer based architectures, leveraging two types of context, local and global. The former describes the paragraph containing the sentence, aiming at solving implicit references, whereas the latter describes the entire document containing the candidate sentence, providing content-based information. The results on three different benchmarks show that the combination of the local and global context in a Transformer model significantly improves the accuracy in Answer Sentence Selection.
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Notes
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Of course, a solution based on a summarization approach would be optimal but poses complicated challenges, which have prevented to obtain better solutions than AS2 (to our knowledge).
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Lauriola, I., Moschitti, A. (2021). Answer Sentence Selection Using Local and Global Context in Transformer Models. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_20
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