Low-Resource Generation of Multi-hop Reasoning Questions (2020)

Abstract: 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.

## introduction question generation (qg) is a hot research topic that aims to create valid and fluent questions corresponding to the answers by fully understanding the semantics on a given text. qg is widely used in many practical scenarios: including providing practice exercises from course materials for educational purposes @xcite , initiating the dialog system by asking questions @xcite , and reducing the labor cost of creating large-scale labeled samples for the qa task @xcite . the mainstream qg methods can be summarized into the rule-based and neural-based models. the first method often transforms the input text into an intermediate symbolic representation, such as a parsing tree, and then convert the resulting form into a question by well-designed templates or general rules @xcite . since rules and templates are hand-crafted, the scalability and generalization of this method are limited. respectively, the neural model usually directly maps the text to question based on neural network @xcite , which is entirely data-driven with far less labor. such a model can be typically regarded as learning a one-to-one mapping between the text and question. the mapping is mainly used to generate simple questions with a single sentence. however, due to the lack of fine-grained modeling on the evidential relations on the text, such a method has minimal capability to form the multihop questions that require sophisticated reasoning skills. these questions have to be grammatically valid. besides, they need to logically correlate with the answers by deducing over multiple entities and relations in several sentences and paragraphs of the given text. as shown in fig .(1), the question asks the director of a film, where the film was shot at the quality cafe in los angeles and todd phillips directed it. these two relations can form a reasoning chain from question to answer by logically integrating the pieces of evidence "los angeles," "quality cafe," and "old school" as well as the pronoun "it" distributed across s 1 in paragraph 1 and s 1 , s 2 in paragraph 2. without capturing such a chain, it is difficult to precisely produce the multi-hop question by using "old school" as a bridging evidence and marginal entity "todd phillips" as the answer. for the task of multi-hop qg, a straightforward solution is to extract a reasoning chain from the input text. under the guidance of the reasoning chain, we learn a neural qg model to make the result satisfy the logical correspondence with the answer. however, the neural model is data-hungry, and the scale of training data mostly limits its performance. each training example is a triple combined with the text, answer, and question. since labeling such a combination is labor-intensive, it is difficult to ensure that we can always obtain sufficient training data in real-world applications. we thus formalize the problem as the low-resource generation of multi-hop questions, which is less explored by existing work. this task has substantial research value since reasoning is crucial in quantifying the high-level cognitive ability of machines, and low resource is the key to promote the extensive application. in order to address the problem, we propose to utilize unlabeled data, which is usually abundant and much easier to obtain. although such data does not combine the questions with the texts and answers, the unlabeled questions contain plentiful expressive forms with structural patterns on the syntax and semantics. these patterns can be seen as the "template" to produce the questions. thus, we can use the patterns as the prior to regularize the qg model and obtain better results accordingly. motivated by the above observations, we propose a practical two-stage approach to learn a multihop qg model from both a small-scale labeled data and a large-size unlabeled corpus. in particular, we first exploit the neural hidden semi-markov model @xcite to parameterize the sophisticated structural patterns on the questions by latent variables. without domain knowledge, the variables can be estimated by maximizing the likelihood of the unlabeled data. we then heuristically extract a reasoning chain from the given text and build a holistic qg model to generate a multi-hop question. the evidential relations in the reasoning chain are leveraged to guide the qg model, so as to let the generated result meet multi-hop logical correspondence with the answer. simultaneously, we naturally incorporate the prior patterns into the qg model. in this way, we can regularize the model and inform it to express a question reasonably. that can improve the syntactic and semantic correctness of the result. with the parameterized patterns, the whole model can be learned from the labeled and unlabeled data in an end-to-end and explainable manner. in order to better balance the supervision of the labeled data and the usage of prior patterns, we propose to optimize the model by reinforcement learning with an augmented evaluated loss. experiments are conducted on the hotpotqa @xcite data set, which contains a large number of reasoning samples with manual annotation. evaluated results in terms of automatic metrics and human judgment show the effectiveness of our approach. moreover, we apply our generated results to the task of machine reading comprehension. we view the results as pseudo-labeled samples to enrich the training data for the task. that can alleviate the labeled data shortage problem and boost the performance accordingly. extensive experiments are performed to show the efficacy of our approach in this application with the help of low-resource qg. the rest of this paper is organized as follows. section 2 elaborates on the proposed low-resource qg framework. section 3 presents experimental results, while section 4 shows the qg application and demonstrates its usefulness. section 5 reviews related works and section 6 concludes this paper. ## approach in this section, we first define some notations and then present the details of the proposed qg framework, including the learning of prior patterns from the unlabeled data, and the multi-hop qg network guided by the reasoning chain and prior patterns. ## evaluations we extensively evaluate the effectiveness of our approach, including the comparisons with state-ofthe-art and the application on a task of mrc-qa. ## conclusions and future works we have proposed an approach to generate the questions required multi-hop reasoning in low-resource conditions. we first built a multi-hop qg model and guided it to satisfy the logical rationality by the reasoning chain extracted from a given text. in order to tackle the labeled data shortage problem, we learned the structural patterns from the unlabeled data by the hidden semi-markov model. with the patterns as a prior, we transferred this fundamental knowledge into the generation model to produce the optimal results. experimental results on the hotpotqa data set demonstrated the effectiveness of our approach. moreover, we explored the generated results to facilitate the real-world application of machine reading comprehension. we will investigate the robustness and scalability of the model.