@inproceedings{varun-etal-2022-trans,
title = "Trans-{KBLSTM}: An External Knowledge Enhanced Transformer {B}i{LSTM} Model for Tabular Reasoning",
author = "Varun, Yerram and
Sharma, Aayush and
Gupta, Vivek",
editor = "Agirre, Eneko and
Apidianaki, Marianna and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
month = may,
year = "2022",
address = "Dublin, Ireland and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.deelio-1.7",
doi = "10.18653/v1/2022.deelio-1.7",
pages = "62--78",
abstract = "Natural language inference on tabular data is a challenging task. Existing approaches lack the world and common sense knowledge required to perform at a human level. While massive amounts of KG data exist, approaches to integrate them with deep learning models to enhance tabular reasoning are uncommon. In this paper, we investigate a new approach using BiLSTMs to incorporate knowledge effectively into language models. Through extensive analysis, we show that our proposed architecture, Trans-KBLSTM improves the benchmark performance on InfoTabS, a tabular NLI dataset.",
}
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%0 Conference Proceedings
%T Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning
%A Varun, Yerram
%A Sharma, Aayush
%A Gupta, Vivek
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland and Online
%F varun-etal-2022-trans
%X Natural language inference on tabular data is a challenging task. Existing approaches lack the world and common sense knowledge required to perform at a human level. While massive amounts of KG data exist, approaches to integrate them with deep learning models to enhance tabular reasoning are uncommon. In this paper, we investigate a new approach using BiLSTMs to incorporate knowledge effectively into language models. Through extensive analysis, we show that our proposed architecture, Trans-KBLSTM improves the benchmark performance on InfoTabS, a tabular NLI dataset.
%R 10.18653/v1/2022.deelio-1.7
%U https://aclanthology.org/2022.deelio-1.7
%U https://doi.org/10.18653/v1/2022.deelio-1.7
%P 62-78
Markdown (Informal)
[Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning](https://aclanthology.org/2022.deelio-1.7) (Varun et al., DeeLIO 2022)
ACL