Papers with SentEval
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. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
| Outcome: | The proposed models outperform the previous best models on both SentEval and SentGLUE transfer tasks. |
Analyzing Word Embedding Through Structural Equation Modeling (2020.lrec-1)
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| Challenge: | Existing studies have shown that word embedding improves accuracy on NLP tasks. |
| Approach: | They propose a causal diagram based on the evaluation results of word embeddings using partial least squares path modeling. |
| Outcome: | The proposed model proves that word embedding contributes to solving downstream tasks. |
SentEval: An Evaluation Toolkit for Universal Sentence Representations (L18-1)
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| Challenge: | a toolkit for evaluating the quality of universal sentence representations is available for download and preprocessing . word embeddings are not trained to perform well on one specific task, but their value lies in their transferability . evaluation of general-purpose word and sentence embeddables has been problematic . |
| Approach: | They propose a toolkit to evaluate the quality of universal sentence representations. |
| Outcome: | The proposed toolkit includes scripts to download and preprocess datasets and an easy interface to evaluate sentence encoders. |
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)
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| Challenge: | Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective. |
| Approach: | They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences . |
| Outcome: | The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations (P19-1)
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| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
| Approach: | They propose to leverage the discourse relations between sentences to curate a high quality sentence relation task by leveraging explicit discourse relations. |
| Outcome: | The proposed model can be used to learn the meaning of two sentences in a bidirectional LSTM sentence encoder. |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
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| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
| Challenge: | specialized embeddings are not available for tasks like entity linking or paragraph classification. |
| Approach: | They evaluate whether universal embeddings can be complemented by specialized embeddables. |
| Outcome: | The proposed embeddings outperform state-of-the-art embeddables without any fine-tuning. |