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Sentence Embedding stands as a fundamental task within the realm of Natural Language Processing, finding extensive application in search engines, expert systems, and question-and-answer platforms.
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 1–14. (2017). \doi
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Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., Zamparelli, R.: A SICK cure for the evaluation of compositional distributional semantic models. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 216–223. (2014)
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Agirre, E., Banea, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Mihalcea, R., Rigau, G., Wiebe, J.: SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 497–511. (2016). \doi
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30
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Williams, A., Nangia, N., Bowman, S.: A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1112–1122. (2018). \doi
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Conneau, A., Kiela, D.: SentEval: An Evaluation Toolkit for Universal Sentence Representations. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation. (2018)
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Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 4171–4186. (2019). \doi
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Reimers, N., Gurevych, I.: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982–3992. (2019). \doi
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in Neural Information Processing Systems 33
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Li, B., Zhou, H., He, J., Wang, M., Yang, Y., Li, L.: On the Sentence Embeddings from Pre-trained Language Models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 9119–9130. (2020). \doi
2023
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2023
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Li, X., Li, J.: Angle-optimized text embeddings. arXiv preprint arXiv:2309.12871 (2023)
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2020
Cited alongside, same era.
Gao, T., Yao, X., Chen, D.: SimCSE: Simple Contrastive Learning of Sentence Embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894–6910. (2021). \doi
2021
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2021
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2022
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35
2022
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Wu, X., Gao, C., Zang, L., Han, J., Wang, Z., Hu, S.: ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 3898–3907. (2022)
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Jiang, T., Jiao, J., Huang, S., Zhang, Z., Wang, D., Zhuang, F., Wei, F., Huang, H., Deng, D., Zhang, Q.: PromptBERT: Improving BERT Sentence Embeddings with Prompts. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 8826–8837. (2022). \doi
2022
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Ni, J., Hernandez Abrego, G., Constant, N., Ma, J., Hall, K., Cer, D., Yang, Y.: Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 1864–1874. (2022). \doi
2022
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2023
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Chen, N., Shou, L., Pei, J., Gong, M., Cao, B., Chang, J., Li, J., Jiang, D.: Alleviating Over-smoothing for Unsupervised Sentence Representation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3552–3566. (2023). \doi
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2023
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2023
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Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: QLoRA: Efficient finetuning of quantized LLMs. Advances in Neural Information Processing Systems 36
2024
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2024
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Agirre, E., Banea, C., Cardie, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W., Lopez-Gazpio, I., Maritxalar, M., Mihalcea, R., Rigau, G., Uria, L., Wiebe, J.: SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 252–263. (2015). \doi
2045
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