Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
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In this paper, we consider the problem of machine reading task when the questions are in the form of keywords, rather than natural language. In recent years, researchers have achieved significant success on machine reading comprehension tasks, such as SQuAD and TriviaQA. These datasets provide a natural language question sentence and a pre-selected passage, and the goal is to answer the question according to the passage. However, in the situation of interacting with machines by means of text, people are more likely to raise a query in form of several keywords rather than a complete sentence. The keyword-based query comprehension is a new challenge, because small variations to a question may completely change its semantical information, thus yield different answers. In this paper, we propose a novel neural network system that consists a Demand Optimization Model based on a passage-attention neural machine translation and a Reader Model that can find the answer given the optimized question. The Demand Optimization Model optimizes the original query and output multiple reconstructed questions, then the Reader Model takes the new questions as input and locate the answers from the passage. To make predictions robust, an evaluation mechanism will score the reconstructed questions so the final answer strike a good balance between the quality of both the Demand Optimization Model and the Reader Model. Experimental results on several datasets show that our framework significantly improves multiple strong baselines on this challenging task.
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