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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On-the-Fly Attention Modulation for Neural Generation
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, selfcontradictory, and often lacks commonsense. Our analyses on sentence-level attention patterns in LMs reveal that neural degeneration may be associated with insufficient learning of task-specific characteristics by the attention mechanism. This finding motivates onthe-fly attention modulation1– a simple but effective method that enables the injection of priors into attention computation during inference. Automatic and human evaluation results on three text generation benchmarks demonstrate that attention modulation helps LMs generate text with enhanced fluency, creativity, and commonsense reasoning, in addition to significantly reduce sentence-level repetition.
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- Award ID(s):
- 1714566
- NSF-PAR ID:
- 10308686
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2021.findings-acl.107
