How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
Table 6: Results on MQR TEST set. The “Ill-formed” shows metric scores for the questions in TEST without rewriting. The next portion shows results for models trained on the TRAIN portion of MQR. The lower portion shows results for methods using other models and/or datasets.
['[EMPTY]', '[BOLD] BLEU-4', '[BOLD] ROUGE-1', '[BOLD] ROUGE-2', '[BOLD] ROUGE-L', '[BOLD] METEOR']
Among these methods models trained on MQR work best. GEC corrects grammatical errors and spelling errors, so it also improves the question quality in rewriting. Round trip neural machine translation is a faithful rewrite of the questions, and it naturally corrects some spelling and grammatical errors during both rounds of translation due to the strong language models present in the NMT models. However, it fails in converting commands and statements into questions. When the two annotators disagree, a judge makes a final decision. According to the annotations, the GEC method slightly improves the question quality scores. However, the GEC model is good at preserving question semantics.