InferLite: Simple Universal Sentence Representations from Natural Language Inference Data (D18-1)
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| Challenge: | InferLite is a lightweight version of InferSent that does not use recurrent layers and can generalize to multiple pre-trained word embeddings. |
| Approach: | They propose a lightweight version of InferSent that does not use recurrent layers and operates on a collection of pre-trained word embeddings. |
| Outcome: | The proposed model outperforms existing models that learn generic embeddings in an unsupervised setting, often requiring several days or weeks to train. |
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| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
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Convolutional Neural Network for Universal Sentence Embeddings (C18-1)
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| Challenge: | Recent studies show that averaging word embeddings is effective for NLP but these models represent a sentence only in terms of features of words or uni-grams. |
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
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Extracting Text Representations for Terms and Phrases in Technical Domains (2023.acl-industry)
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| Challenge: | Large pre-trained language models are extensively used in modern NLP systems. |
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Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
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Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
| Approach: | They propose retrieval focused multilingual sentence embedding models on TensorFlow Hub. |
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SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources . |
| Approach: | They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks. |
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Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference (2022.naacl-main)
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| Challenge: | a method for learning an NLI model is time-consuming and resource-intensive, but it can save time and resources. |
| Approach: | They propose a method for predicting model performance without fine-tuning it . they compare sentence embeddings with cosine similarity to classifiers . |
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LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation (2023.eacl-main)
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| Challenge: | Large-scale language-agnostic sentence embedding models suffer from inference speed and computation overhead. |
| Approach: | They propose to train a lightweight sentence embedding model to achieve this by incorporating knowledge from a teacher model. |
| Outcome: | The proposed model can build low-dimensional sentences for 109 languages with a thin-deep encoder. |