Multi-hop Question Generation with Graph Convolutional Network (2020)

Abstract: Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. on. The code is publicly availableat https://github.com/HLTCHKU

## introduction question generation (qg) is a task to automatically generate a question from a given context and, optionally, an answer. recently, we have observed an increasing interest in text-based qg @xcite @xcite @xcite . most of the existing works on text-based qg focus on generating squad-style @xcite questions, which are generated from the sentence containing the answer or nearby sentences in the same paragraph, via question: what city is the marine air control group 28 (location t) located in? intuitively, there are two main additional challenges needed to be addressed for multi-hop qg. the first challenge is how to effectively identify scattered pieces of evidence that can connect the reasoning path of the answer and question @xcite . as the example shown in previous works mainly focus on single-hop qg, which use neural network based approaches with the sequence-to-sequence (seq2seq) framework. different architectures of encoder and decoder have been designed @xcite to incorporate the information of answer and context to do single-hop reasoning. to the best of our knowledge, none of the previous works address the two challenges we mentioned above for multi-hop qg task. the only work on multi-hop qg @xcite uses multi-task learning with an auxiliary loss for sentence-level supporting fact prediction, requiring supporting fact sentences in different paragraphs being labeled in the training data. while labeling those supporting facts requires heavy human labor and is time-consuming, their method cannot be applied to general multi-hop qg cases without supporting facts. in this paper, we propose a novel architecture named multi-hop encoding fusion network for question generation (mulqg) to address the aforementioned challenges for multi-hop qg. first of all, it extends the seq2seq qg framework from sing-hop to multi-hop for context encoding. additionally, it leverages a graph convolutional network (gcn) on an answer-aware dynamic entity graph, which is constructed from entity mentions in answer and input paragraphs, to aggregate the potential evidence related to the questions. moreover, we use different attention mechanisms to imitate the reasoning procedures of human beings in multihop generation process, the details are explained in section 2. we conduct the experiments on the multi-hop qa dataset hotpotqa @xcite with our model and the baselines. the proposed model outperforms the baselines with a significant improvement on automatic evaluation results, such as bleu @xcite . the human evaluation results further validate that our proposed model is more likely to generate multi-hop questions with high quality in terms of fluency, answerability and completeness scores. our contributions are summarized as follows: ## methodology the intuition is drawn from human's multi-hop question generation process @xcite . firstly, given the answer and context, we skim to establish a general understanding of the texts. then, we find the mentions of entities in or correlated to the answer from the context, and analyse nearby sentences to extract useful evidence. besides, we may also search for linked information in other paragraphs to gain a further understanding of the entities. finally, we coherently fuse our knowledge learned from the previous steps and start to generate questions. to mimic this process, we develop our mulqg framework. the encoding stage is achieved by a novel multi-hop encoder. at the decoding stage, we use maxout pointer decoder as proposed in @xcite . the overview of the framework is shown in figure 1 . ## conclusion multi-hop qg task is more challenging and worthy of exploration compared to conventional singlehop qg. to address the additional challenges in multi-hop qg, we propose mulqg, which does multi-hop context encoding with graph convolutional network and encoding fusion via a gated reasoning module. to the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentencelevel information. the model performance on hot-potqa dataset demonstrates its effectiveness on aggregating scattered pieces of evidence across the paragraphs and fusing information effectively to generate multi-hop questions. the strong reasoning ability of the multi-hop encoder in the mulqa model can potentially be leveraged in complex generation tasks for the future work.