Add to ORKGAnswer 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
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
Semantic matching is a basic problem in natural language processing, but it is far from solved becau...
Answer Sentence Selection (ASS) is one of the steps typically involved in Question Answering, a hard...
Answer Sentence Selection is one of the steps typically involved in Question Answering. Question Ans...
Answer selection is one of the key steps in many Question Answering (QA) applications. In this paper...
Answer selection plays a key role in community question answering (CQA). Previous research on answer...
Answer selection is an important task in community Question Answering (cQA). In recent years, attent...
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving cros...
We implement a state-of-the-art question answering system based on Convolutional Neural Networks and...
In order to assess the degree of intelligence the machine, the machine's understanding of the langu...
State-of-the-art networks that model relations between two pieces of text often use complex architec...
International audienceSince end-to-end deep learning models have started to replace traditional pipe...
Advanced attention mechanisms are an important part of successful neural network approaches for non-...
Since the rise of neural networks in science and industry much progress has been made in the field o...
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
Semantic matching is a basic problem in natural language processing, but it is far from solved becau...
Answer Sentence Selection (ASS) is one of the steps typically involved in Question Answering, a hard...
Answer Sentence Selection is one of the steps typically involved in Question Answering. Question Ans...
Answer selection is one of the key steps in many Question Answering (QA) applications. In this paper...
Answer selection plays a key role in community question answering (CQA). Previous research on answer...
Answer selection is an important task in community Question Answering (cQA). In recent years, attent...
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving cros...
We implement a state-of-the-art question answering system based on Convolutional Neural Networks and...
In order to assess the degree of intelligence the machine, the machine's understanding of the langu...
State-of-the-art networks that model relations between two pieces of text often use complex architec...
International audienceSince end-to-end deep learning models have started to replace traditional pipe...
Advanced attention mechanisms are an important part of successful neural network approaches for non-...
Since the rise of neural networks in science and industry much progress has been made in the field o...
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA...
Semantic matching is a basic problem in natural language processing, but it is far from solved becau...
