Applying deep learning to answer selection: A study and an open task

Abstract

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.

Machine Translation Evaluation Meets Community Question Answering

“…Recently, many neural network (NN) models have been applied to cQA tasks: e.g., question-question similarity dos Santos et al, 2015;Lei et al, 2016) and answer selection (Severyn and Moschitti, 2015;Wang and Nyberg, 2015;Shen et al, 2015;Feng et al, 2015;Tan et al, 2015). Most of these papers concentrate on providing advanced neural architectures in order to better model the problem at hand.…”

Named Entity Recognition in the Medical Domain with Constrained CRF Models

“…For the binary classifier, we make use of IBM's Natural Language Classifier service 3 which is relies on a Convolutional Neural Network combined with word embeddings (Feng et al, 2015). To clarify, the classifiers that we use, while using pretrained word embedding (on general domain), are then only trained with our own training data.…”

An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective

“…Most existing QA systems always output an answer for any question, no matter whether their answer candidate set contains correct answers or not (Feng et al, 2015;Severyn and Moschitti, 2015;Yang et al, 2016;Rao et al, 2016). In practice, however, this can greatly hurt user experience, especially when it is hard for users to judge answer correctness.…”

“…Existing QA systems based on answer selection just select the top-scored candidate as answer, without considering the possibility that the true answer doesn't even exist. However, many neural network models recently explored in the answer existence literature (Hu et al, 2014;Wang and Nyberg, 2015;Feng et al, 2015) could be utilized for answer selection as well in the future. For example, explore the respective advantages of different network architectures such as Long Short-Term Memory Networks (LSTMs) and CNNs.…”