Cross Attention for Selection-based Question Answering.
Alessio Gravina, Federico Rossetto, Silvia Severini, Giuseppe M. Attardi · CINECA IRIS Institutial research information system (University of Pisa) · 2018
Answer Sentence Selection (ASS) is one of the steps typically involved in Question Answering, a hard task for natural language processing since full solutions would require both natural language understanding and world knowledge. We present a new approach to tackle ASS, based on a Cross-Attentive Convolutional Neural Network. The approach was designed for competing in the Fujitsu AI-NLP challenge Fujitsu [4], which evaluates systems on their performance on the SelQA[7] dataset. This dataset was created on purpose as a benchmark to stress the ability of systems to go beyond simple word co-occurrence criteria. Our submission achieved the top score in the challenge.