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        <title>Tackling Temporal Questions in Natural Language Interface to Databases</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Tackling Temporal Questions in Natural Language Interface to Databases
%A Vo, Ngoc Phuoc An
%A Popescu, Octavian
%A Manotas, Irene
%A Sheinin, Vadim
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F vo-etal-2022-tackling
%X 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.
%R 10.18653/v1/2022.emnlp-industry.18
%U https://aclanthology.org/2022.emnlp-industry.18
%U https://doi.org/10.18653/v1/2022.emnlp-industry.18
%P 179-187

Markdown (Informal)

[Tackling Temporal Questions in Natural Language Interface to Databases](https://aclanthology.org/2022.emnlp-industry.18) (Vo et al., EMNLP 2022)

• Tackling Temporal Questions in Natural Language Interface to Databases (Vo et al., EMNLP 2022)

ACL

• Ngoc Phuoc An Vo, Octavian Popescu, Irene Manotas, and Vadim Sheinin. 2022. Tackling Temporal Questions in Natural Language Interface to Databases. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 179–187, Abu Dhabi, UAE. Association for Computational Linguistics.