| Challenge: | Existing approaches to NLP to leverage sparsity have been limited due to the gap with dense representations. |
| Approach: | They propose a Semantic Transformation method to bridge dense and sparse spaces and propose supervised NLP tasks to use both spaces. |
| Outcome: | Experiments with classification tasks and natural language inference tasks show that the proposed method is effective. |
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On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (N19-1)
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| Challenge: | Sense representations target meaning conflation deficiency but their potential impact has not been investigated in downstream NLP applications. |
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SparseFlow: Accelerating Transformers by Sparsifying Information Flows (2024.acl-long)
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Explaining Word Embeddings via Disentangled Representation (2020.aacl-main)
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Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)
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Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations (2020.emnlp-main)
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| Challenge: | Using sparse word embeddings is highly applicable for word sense disambiguation (WSD) . |
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Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
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Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
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Sparse Sequence-to-Sequence Models (P19-1)
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Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval (2025.emnlp-main)
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