Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning

Yerram Varun, Aayush Sharma, Vivek Gupta


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.
Anthology ID:
2022.deelio-1.7
Volume:
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
Venue:
DeeLIO
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–78
Language:
URL:
https://aclanthology.org/2022.deelio-1.7
DOI:
10.18653/v1/2022.deelio-1.7
Bibkey:
Cite (ACL):
Yerram Varun, Aayush Sharma, and Vivek Gupta. 2022. Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 62–78, Dublin, Ireland and Online. Association for Computational Linguistics.
Cite (Informal):
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning (Varun et al., DeeLIO 2022)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.deelio-1.7.pdf
Video:
 https://preview.aclanthology.org/auto-file-uploads/2022.deelio-1.7.mp4
Data
ConceptNetGLUEMultiNLI