Can AI Generate Love Advice?:Toward Neural Answer Generation for Non-Factoid Questions
Table 2: Comparison of AP for answer construction.
['[EMPTY]', '[ITALIC] QA-LSTM', '[ITALIC] Attentive-LSTM', '[ITALIC] Semantic-LSTM', '[ITALIC] Construction', '[ITALIC] Our method']
S5SS4SSS0Px2 Answer Construction We then compared the accuracy of the methods for answer construction. Especially for the answer construction task, the top-1 result is most important since many QA applications show only the top-1 answer. There is no answer construction mechanism in QA-LSTM, Attentive-LSTM, and Semantic-LSTM. Thus we simply merge the conclusion and supplement, each of which has the highest similarity with the question by each method. QA-LSTM and Attentive LSTM are much worse than Semantic-LSTM. This is because the sentences output by Semantic-LSTM are selected by utilizing the words that are emphasized for a context for “Love advice” (i.e. category and titles). Construction is better than Semantic-LSTM since it simultaneously learns the optimum combination of sentences as well as the closeness between the question and sentences. Finally, Our method is better than Construction. This is because it well employs the attention mechanism to link conclusion and supplement sentences and thus the combinations of the sentences are more natural than those of Construction. Our method achieved 20% higher accuracy than QA-LSTM (QA-LSTM marked 0.3262 while Our method marked 0.3901.).