- Kimihiro Hasegawa, Takaaki Matsumoto, R. Takashima, T. Takiguchi, Y. Ariki, T. Mitamura. 2020. Transfer Learning to Generate Multiple Sentence Question with Leveraging Difference between Datasets. Abstract: Recently, question generation (QG) is getting more attention as the dual task of question answering (QA), improving the performance of QA models. The common setting of the text-based QG task is to output a question by taking a context and an answer as input. In this work, we focus on a semantic aspect of generated questions, specifically reasoning, aiming to generate multi-sentence questions (MSQs): questions that require reasoning over multiple sentences (Khashabi et al., 2018), which is more difficult to answer than single-sentence questions (SSQs). In Figure 1, we show the example of MSQ and SSQ. When one answers to the MSQ, one needs to look at the first and second sentences to figure out “Imelda Staunton” is “the English stage and screen actress born in 1956”, who was starred in “The Awakening”, a 2011 British horror film.” Then, one can answer the question by finding the director of “The Awakening.” Contrary to the MSQ which contains several reasoning steps, the SSQ in Figure 1 can be answered by finding the name, looking at only the first sentence. Due to the current success of neural sequence-tosequence approaches, the performances of QG models have been significantly improved on traditional metrics, such as BLEU or ROUGE (Du and Cardie, 2018; Dong et al., 2019). However, considering an application of QG to measure the reading comprehension ability of humans, semantic quality of generated questions is an important issue. One of the semantic-quality problems which existing models fall into is a semantic drift problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. (Zhang and Bansal, 2019) Furthermore, in order to work on reasoning quality of QG, we do not have a parallel dataset that consists of both MSQ and SSQ for the same context and answer. Therefore, in this paper, we propose a transfer-learning QG approach and a method to create a parallel dataset to address the semantic quality, especially reasoning. Then, on the created parallel dataset, we fine-tune a transformer-based pre-trained language model and train a binary classifier context: (1)The Awakening is a 2011 British horror film directed and co-written by Nick Murphy, starring Rebecca Hall, Dominic West, Isaac HempsteadWright and Imelda Staunton. (2)Imelda Mary Philomena Bernadette Staunton, CBE (born 9 January 1956) is an English stage and screen actress. (3)After training at the Royal Academy of Dramatic Art, ... answer: Nick Murphy
