Reinforced Multi-task Approach for Multi-hop Question Generation (2020)

Abstract: Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions.

## introduction in natural language processing (nlp), question generation is considered to be an important yet challenging problem. given a passage and answer as inputs to the model, the task is to generate a semantically coherent question for the given answer. in the past, question generation has been tackled using rule-based approaches such as question templates @xcite or utilizing named entity information and predictive argument structures of sentences @xcite . recently, neural-based approaches have accomplished impressive results @xcite @xcite for the task of question generation. the availability of large-scale machine reading comprehension datasets such as squad @xcite , newsqa @xcite , msmarco @xcite etc. have facilitated research in question answering task. these large-scale datasets have also been used to create the resources @xcite and evaluate the performance @xcite @xcite of the system in low-resource reading comprehension task. squad dataset itself has been the de facto choice for most of the previous works in question generation. however, 90% of the questions in squad can be answered from a single sentence @xcite , hence former qg systems trained on squad are not capable of distilling and utilizing information from multiple sentences. recently released multi-hop datasets such as qangaroo @xcite , complexwebquestions @xcite and hotpotqa @xcite are more suitable for building qg systems that required to gather and utilize information across multiple documents as opposed to a single paragraph or sentence. in multi-hop question answering, one has to reason over multiple relevant sentences from differ-document: a few sects, such as the bishnoi, lay special emphasis on the conservation of particular species, such as the antelope. (ii) ... question shq : who lay special emphasis on conservation of particular species ? document (1): (i) stig lennart blomqvist (born 29 july 1946) is a swedish rally driver. (ii) ... (iii) driving an audi quattro for the audi factory team, blomqvist won the world rally championship drivers' title in 1984 and finished runner-up in 1985. document (2): (i) the audi quattro is a road and rally car, produced by the german automobile manufacturer audi, part of the volkswagen group. (ii) ... question m hq : which car produced by german automobile manufacturer, was driven by stig lennart blomqvist? multi-hop qg has real-world applications in several domains, such as education, chatbots, etc. the questions generated from the multi-hop approach will inspire critical thinking in students by encouraging them to reason over the relationship between multiple sentences to answer correctly. specifically, solving these questions requires higher-order cognitive-skills (e.g., applying, analyzing). therefore, forming challenging questions is crucial for evaluating a student's knowledge and stimulating self-learning. similarly, in goal-oriented chatbots, multi-hop qg is an important skill for chatbots, e.g., in initiating conversations, asking and providing detailed information to the user by considering multiple sources of information. in contrast, in a single-hop qg, only single source of information is considered while generation. in this paper, we propose to tackle multi-hop qg problem in two stages. in the first stage, we learn supporting facts aware encoder representation to predict the supporting facts from the documents by jointly training with question generation and subsequently enforcing the utilization of these supporting facts. the former is achieved by sharing the encoder weights with an answer-aware supporting facts prediction network, trained jointly in a multi-task learning framework. the latter objective is formulated as a question-aware supporting facts prediction reward, which is optimized alongside supervised sequence loss. additionally, we observe that multi-task framework offers substantial improvements in the performance of question generation and also avoid the inclusion of noisy sentences information in generated question, and reinforcement learning (rl) brings the complete and complex question to otherwise maximum likelihood estimation (mle) optimized qg model. our main contributions in this work are: (i). we introduce the problem of multi-hop question generation and propose a multi-task training framework to condition the shared encoder with supporting facts information. (ii). we formulate a novel reward function, multihop-enhanced reward via question-aware supporting fact predictions to enforce the maximum utilization of supporting facts to generate a question; (iii). we introduce an automatic evaluation metric to measure the coverage of supporting facts in the generated question. (iv). empirical results show that our proposed method outperforms the current stateof-the-art single-hop qg models over several automatic and human evaluation metrics on the hotpotqa dataset. ## related work question generation literature can be broadly divided into two classes based on the features used for generating questions. the former regime consists of rule-based approaches @xcite that rely on human-designed features such as named-entity information, etc. to leverage the semantic information from a context for question generation. in the second category, question generation problem is treated as a sequence-to-sequence @xcite learning problem, which involves automatic learning of useful features from the context by leveraging the sheer volume of training data. the first neural encoder-decoder model for question generation was proposed in @xcite . however, this work does not take the answer information into consideration while generating the question. thereafter, several neural-based qg approaches @xcite @xcite have been proposed that utilize the answer position information and copy mechanism. @xcite and @xcite demonstrated an appreciable improvement in the performance of the qg task when trained in a multi-task learning framework. the model proposed by @xcite and @xcite for single-document qa experience a significant drop in accuracy when applied in multiple documents settings. this shortcoming of single-document qa datasets is addressed by newly released multi-hop datasets @xcite @xcite ) that promote multi-step inference across several documents. so far, multi-hop datasets have been predominantly used for answer generation tasks @xcite @xcite . our work can be seen as an extension to single hop question generation where a non-trivial number of supporting facts are spread across multiple documents. ## proposed approach problem statement: in multi-hop question generation, we consider a document list l with n l documents, and an m-word answer a. let the total number of words in all the documents d @xmath0. let a document list l contains a total of k candidate sentences @xmath1, s 2 , . . . , s k } and a set of supporting facts foot_0 sf such that @xmath2. the answer @xmath3, w d a 2 k , . . . , w d am k } is an m-length text span in one of the documents d @xmath4. our task is to generate an n q -word question sequence @xmath5, y 2 , . . . , y n q } whose answer is based on the supporting facts sf in document list l. our proposed model for multi-hop question generation is depicted in figure 1 . ## results and analysis we first describe some variants of our proposed multihop-qg model. the decoder is a single-layer lstm which generates the multi-hop question. ## conclusion in this paper, we have introduced the multi-hop question generation task, which extends the natural language question generation paradigm to multiple document qa. thereafter, we present a novel reward formulation to improve the multi-hop question generation using reinforcement and multi-task learning frameworks. our proposed method performs considerably better than the state-of-the-art question generation systems on hotpotqa dataset. we also introduce sf coverage, an evaluation metric to compare the performance of question generation systems based on their capacity to accumulate information from various documents. overall, we propose a new direction for question generation research with several practical applications. in the future, we will be focusing on to improve the performance of multi-hop question generation without any strong supporting facts supervision.