Ask To The Point: Open-Domain Entity-Centric Question Generation (2023)

Abstract: We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as “what to ask” to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.

## introduction question generation (qg) aims to automatically generate questions from inputs such as raw texts @xcite , knowledge bases @xcite , or images @xcite . particularly, text-based qg broadly benefits conversational chatbots to improve user interaction @xcite , educational materials to enhance reading comprehension @xcite , or qa dataset enrichment to boost qa development @xcite . there are mainly two qg settings, answeraware @xcite and answer-agnostic @xcite , the difference between which is whether answers are known or not. however, in many scenarios we care more about how to ask from an angle, i.e., from an entity of interest (eoi) perspective, rather than ask with an answer or ask randomly, which we refer to as entitycentric question generation (ecqg). for example, in topic-specific learning @xcite , by generating questions focusing on a specified topic entity given a text, we can gain a better understanding of that subject. second, in assisted reading, generating questions pertaining to a specific concept entity serves as reading anchors for efficient content digestion and information localization of desired knowledge @xcite . third, in fact checking, generated questions targeting at different facts of eoi together with obtained answers can further form claims to be supported or refuted @xcite . in this paper, we aim to solve ecqg, which is to generate an entity-centric question given a text and an eoi, emphasizing a particular aspect of the eoi. as answers are usually unknown, i.e., only the entity and its context are given in most scenarios, and unnecessary, i.e., entity alone suffices to locate answers, we define ecqg as answer-agnostic. however, there are several challenges: (1) lack of a dataset for ecqg. (2) lack of centricity, as prior works treated input entities as answers @xcite @xcite rather than as pivots to ask centered at them. (3) lack of rationality, as existing answer-agnostic qg systems suffer from asking irrational, i.e., irrelevant or uninterpretable questions @xcite . summary-enhanced models @xcite have been proposed to alleviate the issue, but they are domain-specific and only apply to detailed and in-depth input such as textbook or news articles, while for open-domain ecqg, where input texts vary in the level of detail, summaries do not always help. (4) lack of answerability, as previous works tried to identify answer phrases to construct questions @xcite @xcite , but such phrases are actually not treated as answers by the model, though it is a strong conditional restric-tion for generation. to address the lack of dataset, we construct a large-scale open-domain ecqg dataset from squad @xcite . to further overcome the centricity, rationality, and answerability challenges, we design a novel generation model with content focusing and question verification (gencone), inspired by the human process of generating questions -humans tend to first identify a focus to form a draft question then verify the question afterward @xcite . specifically, we propose content focusing (cf) and question verification (qv) modules, which are sequentially dependent with the main question generation (qg) module. firstly, the upstream cf module identifies "what to ask", allowing the model to learn focus features as intermediate knowledge that bridges the entity and context, thereby improving question rationality. secondly, the downstream qv module verifies questions through question answering, which imparts answerability-based knowledge into the model, thus improving question answerability. thirdly, gencone jointly encodes entity and context and feeds them into cf, qg, and qv modules, which work together and enforce the model to learn entity-context relation to improve centricity. our main contributions are as follows: (1) we are the first to investigate entity-centric question generation (ecqg) problem. (2) we construct a large-scale open-domain dataset specific for ecqg and make it publicly available. (3) we propose a novel model called gencone, which is among the first works to build a coherent framework with both upstream and downstream sequentially dependent modules for answer-agnostic qg. (4) we conduct extensive experiments to demonstrate the superior performance of gencone and the effectiveness of its components. ## method we propose gencone, a plm-based framework to handle the ecqg task with explicit and implicit guidance. in this section, we first give a formal definition of our problem and then dive into details of model design. ## conclusion we introduce a new task, entity-centric question generation (ecqg), motivated by realistic applications such as topic-specific learning, assisted reading, and fact checking. we also construct a largescale open-domain ecqg dataset from squad. to address rationality, answerability, and centricity issues of generated questions, we propose a coherent plm-based framework called gencone and design two novel modules, content focusing and question verification. experiment results, including both automatic and human evaluations, show that gencone significantly and consistently outperforms baselines in terms of automatic metrics and question quality including entity centricity, rationality, and answerability.