Learning to Paraphrase for Question Answering
Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores.
['[BOLD] Method', '[BOLD] Average F1 (%)  [BOLD] Simple', '[BOLD] Average F1 (%)  [BOLD] Complex']
Finally, we examined how our method fares on simple versus complex questions. We performed this analysis on GraphQuestions as it contains a larger proportion of complex questions. We consider questions that contain a single relation as simple. Complex questions have multiple relations or require aggregation. We observe improvements for both types of questions, with the impact on simple questions being more pronounced. This is not entirely surprising as it is easier to generate paraphrases and predict the paraphrase scores for simpler questions.