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Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasoning. While knowledge graphs (KG) are often used to augment LMs with structured representations of world knowledge, it remains an open question how to effectively fuse and reason over the KG representations and the language context, which provides situational constraints and nuances. In this work, we propose GreaseLM, a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. Information from both modalities propagates to the other, allowing language context representations to be grounded by structured world knowledge, and allowing linguistic nuances (e.g., negation, hedging) in the context to inform the graph representations of knowledge. Our results on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMLE) domains demonstrate that GreaseLM can more reliably answer questions that require reasoning over both situational constraints and structured knowledge, even outperforming models 8x larger.
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An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering
Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems.
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- Award ID(s):
- 1747798
- PAR ID:
- 10131233
- Date Published:
- Journal Name:
- Proceedings of IEEE 20th International Conference on Information Reuse and Integration for Data Science
- Page Range / eLocation ID:
- 326 to 333
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRI.2019.00058
