Papers with binary representation
Word Embedding Binarization with Semantic Information Preservation (2020.coling-main)
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| Challenge: | Word and Sentence embeddings are one of the most common starting points of any NLP task. |
| Approach: | They propose a way to convert word embedding to binary representation to reduce overall size . they propose different approaches suitable for different downstream tasks based on contextual and semantic information. |
| Outcome: | The proposed method reduces the size of the embedding while keeping the semantic and relational knowledge intact. |
Doc2hash: Learning Discrete Latent variables for Documents Retrieval (N19-1)
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| Challenge: | Learning to hash via generative model is a powerful paradigm for fast similarity search in documents retrieval. |
| Approach: | They propose a method that trains a generative model to generate hash codes by using continuous relaxation on priors. |
| Outcome: | The proposed method outperforms other state-of-the-art methods in qualitative and quantitative experiments. |
To be Continuous, or to be Discrete, Those are Bits of Questions (2024.acl-long)
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| Challenge: | Existing studies have shown that binary representations preserve information on the output side, but they are not available for continuous representations. |
| Approach: | They propose to extend the contrastive hashing method to output binary labels instead of continuous vectors. |
| Outcome: | The proposed representation preserves structural information along with label information . it achieves competitive performance on various structured prediction tasks . |
On Eliciting Syntax from Language Models via Hashing (2024.emnlp-main)
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| Challenge: | Unsupervised parsing aims to infer syntactic structure from raw text . despite its importance, advancements in this task have been slow . |
| Approach: | They propose to use unsupervised parsing to infer syntactic structure from raw text . they upgrade the bit-level CKY to first-order to encode lexicon and syntax . |
| Outcome: | The proposed method shows competitive performance on various datasets. |