Answer Selection is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.
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This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.
We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
Ranked #1 on Question Answering on QASent
We apply a general deep learning framework to address the non-factoid question answering task.
We particularly focus on the different comparison functions we can use to match two vectors.
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Ranked #3 on Question Answering on WikiQA
In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction.
Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory.