"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation

• URL: http://arxiv.org/abs/2205.08056v3
• Date: Thu, 19 May 2022 12:33:35 GMT
• Title: "What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation
• Authors: Lingyu Gao, Debanjan Ghosh, Kevin Gimpel
• Abstract summary: We propose a type-controlled framework for inquisitive question generation. We generate a variety of questions that adhere to specific types while drawing from the source texts. We also investigate strategies for selecting a single question from a generated set.
• Score: 35.87102025753666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere to specific types while drawing from the source texts. We also investigate strategies for selecting a single question from a generated set, considering both an informative vs.~inquisitive question classifier and a pairwise ranker trained from a small set of expert annotations. Question selection using the pairwise ranker yields strong results in automatic and manual evaluation. Our human evaluation assesses multiple aspects of the generated questions, finding that the ranker chooses questions with the best syntax (4.59), semantics (4.37), and inquisitiveness (3.92) on a scale of 1-5, even rivaling the performance of human-written questions.

Related papers

• Which questions should I answer? Salience Prediction of Inquisitive Questions [118.097974193544]
  We show that highly salient questions are empirically more likely to be answered in the same article. We further validate our findings by showing that answering salient questions is an indicator of summarization quality in news.
  arXiv  Detail & Related papers  (2024-04-16T21:33:05Z)
• Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations [70.6395572287422]
  Self-alignment method is capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. We conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired.
  arXiv  Detail & Related papers  (2024-02-23T02:24:36Z)
• Qsnail: A Questionnaire Dataset for Sequential Question Generation [76.616068047362]
  We present the first dataset specifically constructed for the questionnaire generation task, which comprises 13,168 human-written questionnaires. We conduct experiments on Qsnail, and the results reveal that retrieval models and traditional generative models do not fully align with the given research topic and intents. Despite enhancements through the chain-of-thought prompt and finetuning, questionnaires generated by language models still fall short of human-written questionnaires.
  arXiv  Detail & Related papers  (2024-02-22T04:14:10Z)
• Generating Answer Candidates for Quizzes and Answer-Aware Question Generators [16.44011627249311]
  We propose a model that can generate a specified number of answer candidates for a given passage of text. Our experiments show that our proposed answer candidate generation model outperforms several baselines.
  arXiv  Detail & Related papers  (2021-08-29T19:33:51Z)
• Controllable Open-ended Question Generation with A New Question Type Ontology [6.017006996402699]
  We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation.
  arXiv  Detail & Related papers  (2021-07-01T00:02:03Z)
• GooAQ: Open Question Answering with Diverse Answer Types [63.06454855313667]
  We present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google.
  arXiv  Detail & Related papers  (2021-04-18T05:40:39Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation [17.819949636876018]
  We propose two new strategies to deal with the answer-agnostic question generation task: question type prediction and copy loss mechanism. The question type module is to predict the types of questions that should be asked, which allows our model to generate multiple types of questions for the same source sentence. The new copy loss enhances the original copy mechanism to make sure that every important word in the source sentence has been copied when generating questions.
  arXiv  Detail & Related papers  (2020-05-24T07:09:04Z)
• Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus [23.676748207014903]
  We propose Answer-Clue-Style-aware Question Generation (ACS-QG) It aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale. We can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
  arXiv  Detail & Related papers  (2020-01-27T05:27:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.