Question Answering is a Format; When is it Useful?
arXiv:1909.11291
Abstract
Recent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to image and video understanding. With this expansion, there are many differing views on the utility and definition of "question answering" itself. Some argue that its scope should be narrow, or broad, or that it is overused in datasets today. In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself. We discuss when a task is correctly described as question answering, and when a task is usefully posed as question answering, instead of using some other format.
References in corpus (6)
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Cited by in corpus (11)
- QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
- Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
- Description Based Text Classification with Reinforcement Learning
- TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
- Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction
- Quizbowl: The Case for Incremental Question Answering
- TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
- Knowledge Guided Named Entity Recognition for BioMedical Text
- Bridging Anaphora Resolution as Question Answering
- Language (Re)modelling: Towards Embodied Language Understanding
- Is Sluice Resolution really just Question Answering?