LSTM-based Deep Learning Models for Non-factoid Answer Selection
This work addresses answer selection in domains like customer support, but it is incremental as it builds on existing deep learning frameworks.
The paper tackled answer selection for non-factoid questions by applying deep learning models based on biLSTM, CNN, and attention mechanisms, achieving substantial performance improvements over strong baselines on TREC-QA and InsuranceQA datasets.
In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.