Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator (C18-1)
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| Challenge: | a novel method for obtaining sentence-level embeddings is proposed . the problem of obtaining a semantic embeddable sentence is at the core of understanding languages . |
| Approach: | They propose a method for obtaining sentence-level embeddings by using a sequential encoder-decoder framework. |
| Outcome: | The proposed method outperforms the state-of-the-art on a sentiment analysis task. |
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| Challenge: | Existing methods to learn fixed-length embeddings for sentence semantics require large computational cost, making it difficult to process billions of sentences cost-efficiently or deploy models on resource-constrained devices such as smartphones. |
| Approach: | They propose to extract word embeddings from a pre-trained Sentence Transformer and improve them with sentence-level principal component analysis followed by knowledge distillation or contrastive learning. |
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A Bilingual Generative Transformer for Semantic Sentence Embedding (2020.emnlp-main)
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| Challenge: | Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embeddable space indicates closeness of semantics between the sentences. |
| Approach: | They propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. |
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Going Beyond Sentence Embeddings: A Token-Level Matching Algorithm for Calculating Semantic Textual Similarity (2023.acl-short)
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| Challenge: | Semantic Textual Similarity (STS) measures the degree to which the underlying semantics of paired sentences are equivalent. |
| Approach: | They propose a token-level matching inference algorithm which can be applied on top of any language model to improve its performance on STS task. |
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Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
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Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Pre-trained language models have been successful on a wide range of NLP tasks . however, contextual representations from pre-trated models contain entangled semantic and syntactic information. |
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More Discriminative Sentence Embeddings via Semantic Graph Smoothing (2024.eacl-short)
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| Challenge: | Text categorization is a natural language processing task that involves arranging texts into coherent groups based on their content. |
| Approach: | They propose to use semantic graph smoothing to enhance sentence embeddings from pretrained models to improve results for supervised and unsupervised document categorization tasks. |
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Exploring Semantic Properties of Sentence Embeddings (P18-2)
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| Challenge: | Neural vector representations are ubiquitous throughout all subfields of natural language processing. |
| Approach: | They propose a framework that generates triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect their similarity. |
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Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
| Approach: | They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text. |
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On the Dimensionality of Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing work focuses on improving the quality of sentence embeddings, but the exploration of sentence dimension is limited. |
| Approach: | They propose a two-step training method where the encoder and pooler are optimized separately to mitigate the overall performance loss in low-dimension scenarios. |
| Outcome: | The proposed method significantly improves the performance of low-dimensional sentence embeddings on seven STS tasks and seven sentence classification tasks. |
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. |
| Outcome: | The proposed method achieves 3.7% to 6.4% Pearson’s r over single-source word embeddings on the STS Benchmark and on the StS12-STS16 datasets. |