2022
EMNLP
EMNLP 2022
Tackling Temporal Questions in Natural Language Interface to Databases
Abstract
AbstractTemporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). This paper addresses and examines how temporal questions being studied and supported by the research community at both levels: popular annotated dataset (e.g. Spider) and recent advanced models. We present a new dataset with accompanied databases supporting temporal questions in NLIDB. We experiment with two SOTA models (Picard and ValueNet) to investigate how our new dataset helps these models learn and improve performance in temporal aspect.
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Interdisciplinary Bridge
— Artificial Intelligence and Natural Language Processing
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Keyword Pioneer
— temporal question
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Information Extraction
Natural Language Processing > Applications > Machine Reading Comprehension
Natural Language Processing > Applications > Question Answering
Natural Language Processing > Applications > Semantic Parsing
Artificial Intelligence > Core AI > Natural Language Processing