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Language understanding involves processing text with both the grammatical and 2 common-sense contexts of the text fragments. The text “I went to the grocery store 3 and brought home a car” requires both the grammatical context (syntactic) and 4 common-sense context (semantic) to capture the oddity in the sentence. Contex5 tualized text representations learned by Language Models (LMs) are expected to 6 capture a variety of syntactic and semantic contexts from large amounts of training 7 data corpora. Recent work such as ERNIE has shown that infusing the knowl8 edge contexts, where they are available in LMs, results in significant performance 9 gains on General Language Understanding (GLUE) benchmark tasks. However, 10 to our knowledge, no knowledge-aware model has attempted to infuse knowledge 11 through top-down semantics-driven syntactic processing (Eg: Common-sense to 12 Grammatical) and directly operated on the attention mechanism that LMs leverage 13 to learn the data context. We propose a learning framework Top-Down Language 14 Representation (TDLR) to infuse common-sense semantics into LMs. In our 15 implementation, we build on BERT for its rich syntactic knowledge and use the 16 knowledge graphs ConceptNet and WordNet to infuse semantic knowledge.
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KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
With the advent of pre-trained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pre-trained LMs. While existing approaches leverage external knowledge, it remains an open question how to jointly incorporate knowledge graphs represented in varying contexts — from local (e.g., sentence), document-level, to global knowledge, to enable knowledge-rich and interpretable exchange across contexts. In addition, incorporating varying contexts can especially benefit long document understanding tasks that leverage pre-trained LMs, typically bounded by the input sequence length. In light of these challenges, we propose KALM, a language model that jointly leverages knowledge in local, document-level, and global contexts for long document understanding. KALM firstly encodes long documents and knowledge graphs into the three knowledge-aware context representations. KALM then processes each context with context-specific layers. These context-specific layers are followed by a ContextFusion layer that facilitates knowledge exchange to derive an overarching document representation. Extensive experiments demonstrate that KALM achieves state-of-the-art performance on three long document understanding tasks across 6 datasets/settings. Further analyses reveal that the three knowledge-aware contexts are complementary and they all contribute to model performance, while the importance and information exchange patterns of different contexts vary on different tasks and datasets.
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- PAR ID:
- 10433151
- Date Published:
- Journal Name:
- ACL: Annual Meeting of the Association for Computational Linguistics
- Page Range / eLocation ID:
- 2116–2138
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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