Related papers: Stronger Transformers for Neural Multi-Hop Question Generation

Stronger Transformers for Neural Multi-Hop Question Generation

URL: http://arxiv.org/abs/2010.11374v1
Date: Thu, 22 Oct 2020 01:51:09 GMT
Title: Stronger Transformers for Neural Multi-Hop Question Generation
Authors: Devendra Singh Sachan and Lingfei Wu and Mrinmaya Sachan and William Hamilton
Abstract summary: We introduce a series of strong transformer models for multi-hop question generation. We show that we can substantially outperform the state-of-the-art by 5 BLEU points using a standard transformer architecture.
Score: 48.06692942528804
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Prior work on automated question generation has almost exclusively focused on generating simple questions whose answers can be extracted from a single document. However, there is an increasing interest in developing systems that are capable of more complex multi-hop question generation, where answering the questions requires reasoning over multiple documents. In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text. While prior work has emphasized the importance of graph-based models, we show that we can substantially outperform the state-of-the-art by 5 BLEU points using a standard transformer architecture. We further demonstrate that graph-based augmentations can provide complimentary improvements on top of this foundation. Interestingly, we find that several important factors--such as the inclusion of an auxiliary contrastive objective and data filtering could have larger impacts on performance. We hope that our stronger baselines and analysis provide a constructive foundation for future work in this area.

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