Query-based Attention CNN for Text Similarity Map
This addresses question answering for movie-related queries, but it is incremental as it builds on existing CNN and attention methods.
The paper tackles question answering by introducing Query-based Attention CNN (QACNN) for Text Similarity Map, achieving 79.99% accuracy on the MovieQA dataset, which is state-of-the-art.
In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.