EMNLP 2022industry2 citations
Tackling Temporal Questions in Natural Language Interface to Databases
Ngoc Phuoc An Vo, Octavian Popescu, Irene Manotas, Vadim Sheinin
Abstract
Temporal 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.
BibTeX
@inproceedings{vo-etal-2022-tackling,
title = "Tackling Temporal Questions in Natural Language Interface to Databases",
author = "Vo, Ngoc Phuoc An and
Popescu, Octavian and
Manotas, Irene and
Sheinin, Vadim",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.18/",
doi = "10.18653/v1/2022.emnlp-industry.18",
pages = "179--187"
}