Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations

• URL: http://arxiv.org/abs/2311.04335v1
• Date: Tue, 7 Nov 2023 20:38:30 GMT
• Title: Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
• Authors: Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu
• Abstract summary: Sub-sentence encoder is a contrastively-learned contextual embedding model for fine-grained semantic representation of text. We show that sub-sentence encoders keep the same level of inference cost and space complexity compared to sentence encoders.
• Score: 102.05351905494277
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text. In contrast to the standard practice with sentence embeddings, where the meaning of an entire sequence of text is encoded into a fixed-length vector, the sub-sentence encoder learns to produce distinct contextual embeddings corresponding to different atomic propositions, i.e. atomic units of meaning expressed within a text sequence. The sub-sentence embeddings are contrastively learned to recognize (inferred) semantic equivalence between propositions across different text sequences. Our experiments show the effectiveness of sub-sentence encoders in applications, such as retrieving supporting facts for fine-grained text attribution or recognizing the conditional semantic similarity between texts. In practice, we demonstrate that sub-sentence encoders keep the same level of inference cost and space complexity compared to sentence encoders.

Related papers

• RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank [54.854714257687334]
  We propose a novel approach, RankCSE, for unsupervised sentence representation learning. It incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks.
  arXiv  Detail & Related papers  (2023-05-26T08:27:07Z)
• Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations [80.45474362071236]
  It is unclear whether the compositional semantics of sentences can be directly reflected as compositional operations in the embedding space. We propose InterSent, an end-to-end framework for learning interpretable sentence embeddings.
  arXiv  Detail & Related papers  (2023-05-24T00:44:49Z)
• Textual Entailment Recognition with Semantic Features from Empirical Text Representation [60.31047947815282]
  A text entails a hypothesis if and only if the true value of the hypothesis follows the text. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair.
  arXiv  Detail & Related papers  (2022-10-18T10:03:51Z)
• Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders [4.254099382808598]
  We propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans. Empirical results show that the alignment component brings in both improved performance and interpretability for various sentence encoders.
  arXiv  Detail & Related papers  (2022-10-11T09:52:52Z)
• A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings [28.046786376565123]
  We propose a semantics-aware contrastive learning framework for sentence embeddings, termed Pseudo-Token BERT (PT-BERT) We exploit the pseudo-token space (i.e., latent semantic space) representation of a sentence while eliminating the impact of superficial features such as sentence length and syntax. Our model outperforms the state-of-the-art baselines on six standard semantic textual similarity (STS) tasks.
  arXiv  Detail & Related papers  (2022-03-11T12:29:22Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• Sequence-to-Sequence Contrastive Learning for Text Recognition [29.576864819760498]
  We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations. Experiments on handwritten text and on scene text show that when a text decoder is trained on the learned representations, our method outperforms non-sequential contrastive methods.
  arXiv  Detail & Related papers  (2020-12-20T09:07:41Z)
• A Comparative Study on Structural and Semantic Properties of Sentence Embeddings [77.34726150561087]
  We propose a set of experiments using a widely-used large-scale data set for relation extraction. We show that different embedding spaces have different degrees of strength for the structural and semantic properties. These results provide useful information for developing embedding-based relation extraction methods.
  arXiv  Detail & Related papers  (2020-09-23T15:45:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.