Subspace Representations for Soft Set Operations and Sentence Similarities (2024.naacl-long)
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| Challenge: | Embedding-based word representations are crucial for capturing the semantic meanings of individual words. |
| Approach: | They propose to embed word sets and corresponding set operations within pre-trained word embedding spaces. |
| Outcome: | The proposed representations outperform vector-based representations in sentence similarity and set retrieval tasks. |
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Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
| Challenge: | Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc. |
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Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations (2023.emnlp-main)
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| Challenge: | Existing approaches to learn sentence embeddings do not capture the semantic similarity of sentences. |
| Approach: | They propose a framework that integrates compositional sentence operations into the embedding space and optimizes operator networks and a bottleneck encoder-decoder model to produce meaningful and interpretable sentence embeddables. |
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Correlations between Word Vector Sets (D19-1)
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| Challenge: | Similarity measures based on word embeddings are easily competing with deep learning and expert-engineered systems on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a new approach to measure word embeddings using pooling operations and correlation coefficients instead of pooling . they also propose centered kernel alignment as a natural generalisation of squared cosine similarity for sets of word vectors. |
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Tsetlin Machine Embedding: Representing Words Using Logical Expressions (2024.findings-eacl)
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| Challenge: | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing. |
| Approach: | They propose to embed words in vector space using propositional logic instead of dense vectors . they evaluate embeddings on intrinsic and extrinsic benchmarks and visualize word clusters based on their results . |
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Parameter-free Sentence Embedding via Orthogonal Basis (D19-1)
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| Challenge: | Existing methods to build sentence embeddings are parameterized and require training to optimize their parameters. |
| Approach: | They propose a non-parameterized method to combine pre-trained word embeddings into sentence representations using an orthogonal basis of the word vector subspace and its surrounding context. |
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Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
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Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast (2021.emnlp-main)
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| Challenge: | Existing work uses sentences within the same batch as negatives, which suffers from easy negatives. |
| Approach: | They propose to align sentence representations from different languages into a unified embedding space . they adapt MoCo to further improve the quality of alignment . |
| Outcome: | The proposed model achieves state-of-the-art on several tasks. |