@inproceedings{kannen-etal-2023-best,
title = "Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction",
author = "Kannen, Nithish and
Sharma, Udit and
Neelam, Sumit and
Khandelwal, Dinesh and
Ikbal, Shajith and
Karanam, Hima and
Subramaniam, L",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.287",
doi = "10.18653/v1/2023.emnlp-main.287",
pages = "4729--4744",
abstract = "Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events. Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA. One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB and entity/relation linking errors. A failure to fetch even a single fact will block KBQA from computing the answer. Such cases of KB incompleteness are even more profound in the temporal context. To address this issue, we explore an interesting direction where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB. We model the extraction problem as an open-domain question answering task using off-the-shelf language models. This way, we target to extract from textual resources those facts that failed to get retrieved from the KB. Experimental results on two temporal QA benchmarks show promising {\textasciitilde}30{\%} {\&} {\textasciitilde}10{\%} relative improvements in answer accuracies without any additional training cost.",
}
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<abstract>Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events. Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA. One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB and entity/relation linking errors. A failure to fetch even a single fact will block KBQA from computing the answer. Such cases of KB incompleteness are even more profound in the temporal context. To address this issue, we explore an interesting direction where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB. We model the extraction problem as an open-domain question answering task using off-the-shelf language models. This way, we target to extract from textual resources those facts that failed to get retrieved from the KB. Experimental results on two temporal QA benchmarks show promising ~30% & ~10% relative improvements in answer accuracies without any additional training cost.</abstract>
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%0 Conference Proceedings
%T Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction
%A Kannen, Nithish
%A Sharma, Udit
%A Neelam, Sumit
%A Khandelwal, Dinesh
%A Ikbal, Shajith
%A Karanam, Hima
%A Subramaniam, L.
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kannen-etal-2023-best
%X Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events. Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA. One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB and entity/relation linking errors. A failure to fetch even a single fact will block KBQA from computing the answer. Such cases of KB incompleteness are even more profound in the temporal context. To address this issue, we explore an interesting direction where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB. We model the extraction problem as an open-domain question answering task using off-the-shelf language models. This way, we target to extract from textual resources those facts that failed to get retrieved from the KB. Experimental results on two temporal QA benchmarks show promising ~30% & ~10% relative improvements in answer accuracies without any additional training cost.
%R 10.18653/v1/2023.emnlp-main.287
%U https://aclanthology.org/2023.emnlp-main.287
%U https://doi.org/10.18653/v1/2023.emnlp-main.287
%P 4729-4744
Markdown (Informal)
[Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction](https://aclanthology.org/2023.emnlp-main.287) (Kannen et al., EMNLP 2023)
ACL