Joint Learning of Answer Selection and Answer Summary Generation in Community Question AnsweringThis work was financially supported by the National Natural Science Foundation of China (No.61602013) and a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418).
Table 3: Evaluation on Answer Selection
['[BOLD] Models', '[BOLD] MAP', '[BOLD] MRR']
We show that the joint learning model (ASAS) achieves state-of-the-art performance. There are several notable observations in the results. (i ) BM25 model and even the basic deep learning model slightly improve the performance compared to random guessing, which signifies that the testing set is indeed a difficult one. (ii) The Compare-Aggregate methods (including CA and COALA) and AP-BiLSTM, which have been proven to be relatively effective in long-sentence answer selection [COALA, dos2016attentive], outperforms other strong baseline methods. (iii) Although Two-Stage methods actually improve the final answer selection result, it is time-consuming and inconvenient to train two separate models. In specific, using gold summary (GOLD) achieves the best performance, and Question-driven PGN (QPGN) performs better than original PGN. With the same summarization method, different answer selection models achieve similar results. (iv) Finally, the proposed joint learning model (ASAS) decently and substantially enhances the performance, which not only achieves the SOTA result, but also is easily trained by end-to-end fashion. By doing so, we precisely pick out the correct answers from candidate answers with long sentences, and meanwhile generate abstractive summaries for the convenience of community users. (v) The ablation study shows both the two-way attention mechanism and the pointer network contribute to the final result. The two-way attention mechanism enhances the interaction between questions and decoded answer summaries, while the pointer network aids in generating a better summary.