Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
This work addresses a specific bottleneck in question generation for NLP applications like QA systems, representing an incremental improvement.
The paper tackled the problem of generating questions in NLP by proposing a pipelined system that separates interrogative word prediction from question generation, achieving new state-of-the-art results on SQuAD with improvements such as BLEU-1 from 46.58 to 47.69 and ROUGE-L from 44.53 to 46.94.
Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.