Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval

• URL: http://arxiv.org/abs/2010.11915v2
• Date: Fri, 4 Jun 2021 19:13:41 GMT
• Title: Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval
• Authors: Akari Asai and Eunsol Choi
• Abstract summary: We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise. Our controlled experiments suggest two headrooms -- paragraph selection and answerability prediction. We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer.
• Score: 46.3246135936476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent pretrained language models "solved" many reading comprehension benchmarks, where questions are written with access to the evidence document. However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging. We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise, on Natural Questions and TyDi QA. Our controlled experiments suggest two headrooms -- paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not. When provided with a gold paragraph and knowing when to abstain from answering, existing models easily outperform a human annotator. However, predicting answerability itself remains challenging. We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer. With this new data, we conduct per-category answerability prediction, revealing issues in the current dataset collection as well as task formulation. Together, our study points to avenues for future research in information-seeking question answering, both for dataset creation and model development.

Related papers

• 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)
• PCoQA: Persian Conversational Question Answering Dataset [12.07607688189035]
  The PCoQA dataset is a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. PCoQA is designed to present novel challenges compared to previous question answering datasets. This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models.
  arXiv  Detail & Related papers  (2023-12-07T15:29:34Z)
• CREPE: Open-Domain Question Answering with False Presuppositions [92.20501870319765]
  We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections. We show that adaptations of existing open-domain QA models can find presuppositions moderately well, but struggle when predicting whether a presupposition is factually correct.
  arXiv  Detail & Related papers  (2022-11-30T18:54:49Z)
• Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices [0.0]
  The research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailed followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language.
  arXiv  Detail & Related papers  (2021-12-07T08:53:40Z)
• Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections [35.005593397252746]
  A key challenge in building and evaluating models for discourse comprehension is the lack of annotated data. This paper presents a novel paradigm that enables scalable data collection targeting the comprehension of news documents. The resulting corpus, DCQA, consists of 22,430 question-answer pairs across 607 English documents.
  arXiv  Detail & Related papers  (2021-11-01T04:50:26Z)
• ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers [93.55268936974971]
  We describe a Question Answering dataset that contains complex questions with conditional answers. We call this dataset ConditionalQA. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions.
  arXiv  Detail & Related papers  (2021-10-13T17:16:46Z)
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers [66.11048565324468]
  We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
  arXiv  Detail & Related papers  (2021-05-07T00:12:34Z)
• 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)

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.