@inproceedings{minhas-etal-2022-xinfotabs,
title = "{XI}nfo{T}ab{S}: Evaluating Multilingual Tabular Natural Language Inference",
author = "Minhas, Bhavnick and
Shankhdhar, Anant and
Gupta, Vivek and
Aggarwal, Divyanshu and
Zhang, Shuo",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.fever-1.7",
doi = "10.18653/v1/2022.fever-1.7",
pages = "59--77",
abstract = "The ability to reason about tabular or semi-structured knowledge is a fundamental problem for today{'}s Natural Language Processing (NLP) systems. While significant progress has been achieved in the direction of tabular reasoning, these advances are limited to English due to the absence of multilingual benchmark datasets for semi-structured data. In this paper, we use machine translation methods to construct a multilingual tabular NLI dataset, namely XINFOTABS, which expands the English tabular NLI dataset of INFOTABS to ten diverse languages. We also present several baselines for multilingual tabular reasoning, e.g., machine translation-based methods and cross-lingual. We discover that the XINFOTABS evaluation suite is both practical and challenging. As a result, this dataset will contribute to increased linguistic inclusion in tabular reasoning research and applications.",
}
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<abstract>The ability to reason about tabular or semi-structured knowledge is a fundamental problem for today’s Natural Language Processing (NLP) systems. While significant progress has been achieved in the direction of tabular reasoning, these advances are limited to English due to the absence of multilingual benchmark datasets for semi-structured data. In this paper, we use machine translation methods to construct a multilingual tabular NLI dataset, namely XINFOTABS, which expands the English tabular NLI dataset of INFOTABS to ten diverse languages. We also present several baselines for multilingual tabular reasoning, e.g., machine translation-based methods and cross-lingual. We discover that the XINFOTABS evaluation suite is both practical and challenging. As a result, this dataset will contribute to increased linguistic inclusion in tabular reasoning research and applications.</abstract>
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%0 Conference Proceedings
%T XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference
%A Minhas, Bhavnick
%A Shankhdhar, Anant
%A Gupta, Vivek
%A Aggarwal, Divyanshu
%A Zhang, Shuo
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F minhas-etal-2022-xinfotabs
%X The ability to reason about tabular or semi-structured knowledge is a fundamental problem for today’s Natural Language Processing (NLP) systems. While significant progress has been achieved in the direction of tabular reasoning, these advances are limited to English due to the absence of multilingual benchmark datasets for semi-structured data. In this paper, we use machine translation methods to construct a multilingual tabular NLI dataset, namely XINFOTABS, which expands the English tabular NLI dataset of INFOTABS to ten diverse languages. We also present several baselines for multilingual tabular reasoning, e.g., machine translation-based methods and cross-lingual. We discover that the XINFOTABS evaluation suite is both practical and challenging. As a result, this dataset will contribute to increased linguistic inclusion in tabular reasoning research and applications.
%R 10.18653/v1/2022.fever-1.7
%U https://aclanthology.org/2022.fever-1.7
%U https://doi.org/10.18653/v1/2022.fever-1.7
%P 59-77
Markdown (Informal)
[XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference](https://aclanthology.org/2022.fever-1.7) (Minhas et al., FEVER 2022)
ACL