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 , which evaluates systems on their performance on the SelQA 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.
|Titolo:||Cross Attention for Selection-based Question Answering|
|Anno del prodotto:||2018|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|