Can AI Generate Love Advice?:Toward Neural Answer Generation for Non-Factoid Questions
Table 1: Comparison of AP for answer selection.
['[EMPTY]', '[ITALIC] QA-LSTM', '[ITALIC] Attentive-LSTM', '[ITALIC] Semantic-LSTM', '[ITALIC] Construction', '[ITALIC] Our method']
Answer Selection We first compare the accuracy of methods for answer selection. QA-LSTM and Attentive LSTM are worse than Semantic-LSTM. This indicates that Semantic-LSTM can incorporate semantic information (titles/categories) into word embeddings; it can emphasize words according to the context they appeared and thus the matching accuracy between question vector and conclusion (supplement) vector was improved. Attentive LSTM is worse than QA-LSTM as described above. Construction and Our method are better than Semantic-LSTM. This is because they can avoid the combinations of sentences that are not good enough even though the scores of closeness between questions and sentences are high. This implies that, if the combination is not good, the selection of answer sentences also tends to be erroneous. Finally, Our method, which provides sophisticated selection/combination strategies, yielded higher accuracy than the other methods. It achieved 4.4% higher accuracy than QA-LSTM (QA-LSTM marked 0.8472 while Our method marked 0.8846.).