Low-Resource Generation of Multi-hop Reasoning Questions
By Jianxing Yu, Wei David Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin. 2020
15 citations recorded by the source corpus.
This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text. Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain. Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics. These patterns can be estimated by the neural hidden semi-Markov model using latent variables. With latent patterns as a prior, we can regularize the generation model and produce the optimal results. Experimental results on the HotpotQA data set demonstrate the effectiveness of our model. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
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Citations in the corpus
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm (2021, background): “This problem has been recognized in previous work which has sought to allow serial reasoning using specialized neural network architectures [7, 26].”