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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.deelio-1.7.pdf
- Data
- ConceptNet, GLUE, MultiNLI