Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021)

Abstract: Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.

## introduction automatic generation of question-answer pairs (qa pairs) is a widely studied problem, primarily used to improve the performance of question answering systems via data augmentation @xcite . however, question generation has also recently garnered interest in the context of conversational agents, where suggested questions (sqs) (i.e., you can also ask...) have emerged as a promising approach to drive multi-turn dialogues by educating customers about the agent capabilities and guiding users along dialogue trajectories with more engaging content @xcite . as an example, consider a news chatbot engaged in a dialogue regarding covid-19 vaccine developments producing the sq {q: how effective is the pfizer-biontech vaccine?} paired with the answer {a: pfizer/biontech vaccine is around 91% effective at preventing covid-19, according to updated trial data. experts fear new variants of covid-19 from south africa and brazil may be resistant to existing vaccines and treatment.} firstly, sqs of this form mitigates the user burden regarding the necessity of both deep subject knowledge to ask good questions and awareness of the agent question answering capabilities to expect good answers. secondly, the agent can look-ahead when selecting sqs to bias toward confidently correct answers and content expected to lead to further follow-up questions and general system engagement. targeting the sq problem in news chatbot scenarios (e.g., @xcite ), this work examines qa pair generation corresponding to a news article summary paired with a self-contained question. to satisfy these requirements, we first collect a corpus of suitable qa pairs, accomplished by curating a set of news articles with well-formed questions as their titles and for which we can confidently generate variable length summaries as an-article: president biden's infrastructure plan calls for an unprecedented boost in federal aid to the nation's passenger rail system, seeking to address amtrak's repair backlog, extend service to more cities and modernize the network in the northeast corridor. the american jobs plan announced wednesday calls for $80 billion for rail -money that could be crucial in taking passenger service to cities such as las vegas and nashville, and expand operations across large metropolitan areas such as atlanta and houston. "president biden's infrastructure plan is what this nation has been waiting for," amtrak chief executive william j. flynn said, while echoing biden's push to rebuild and improve... suggested question: what does president biden's infrastructure plan mean for amtrak? short answer: the federal funding would help amtrak accomplish long-needed upgrades to tracks, tunnels and bridges in the northeast. ## (sc) 2 qa: a self-contained and summary-centric qa dataset while multiple qa datasets exist to train a qg or ag model, none specifically fit the goal of this paper. qa pairs in squad @xcite , newsqa @xcite , and natural questions (nq) @xcite are not designed to capture the article gists, and a significant number of questions in squad and newsqa are not self-contained. a key observation enabling this work is that many news articles have questions as their titles (e.g. how has the biden administration helped student loan borrowers?) that can be used to train a sq generation model since these questions usually correspond to the central gists of the news articles and are designed to be understood without reading the articles. however, two challenges remain: (1) clickbait titles need to be filtered, and 2) these questions are not paired with summary-centric answers. therefore, we developed the following data collection procedure to produce (sc) 2 qa, our selfcontained summary-centric qa dataset.