Hashing based Answer Selection

Answer selection is an important subtask of question answering (QA), where deep models usually achieve better performance. Most deep models adopt question-answer interaction mechanisms, such as attention, to get vector representations for answers. When these interaction based deep models are deployed for online prediction, the representations of all answers need to be recalculated for each question. This procedure is time-consuming for deep models with complex encoders like BERT which usually have better accuracy than simple encoders. One possible solution is to store the matrix representation (encoder output) of each answer in memory to avoid recalculation. But this will bring large memory cost. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. HAS adopts a hashing strategy to learn a binary matrix representation for each answer, which can dramatically reduce the memory cost for storing the matrix representations of answers. Hence, HAS can adopt complex encoders like BERT in the model, but the online prediction of HAS is still fast with a low memory cost. Experimental results on three popular answer selection datasets show that HAS can outperform existing models to achieve state-of-the-art performance

Proposed an Optimal Search Algorithm to Find the Best Answer in a Question Answering Systems

QA systems extract answers in natural language question from a large set of documents. In this paper, we will design and implement Restricted Domain QA System based on a knowledge database. In this system we will use a genetic algorithm and optimal-genetic algorithm to search in the knowledge base for finding the answers. Web pages are sources of knowledge system. To validate the proposed approach, we will implement these algorithms; results indicate a significant increase in accuracy of the proposed system compare to previous systems