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@@ -212,12 +212,18 @@ Details about data, training, evaluation and performance metrics are available i
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  ### BibTeX entry and citation info
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  ```bibtex
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- @misc{mao2023lealla,
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- title={LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation},
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- author={Zhuoyuan Mao and Tetsuji Nakagawa},
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- year={2023},
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- eprint={2302.08387},
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- archivePrefix={arXiv},
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- primaryClass={cs.CL}
 
 
 
 
 
 
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  }
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  ```
 
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  ### BibTeX entry and citation info
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  ```bibtex
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+ @inproceedings{mao-nakagawa-2023-lealla,
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+ title = "{LEALLA}: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation",
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+ author = "Mao, Zhuoyuan and
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+ Nakagawa, Tetsuji",
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+ booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
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+ month = may,
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+ year = "2023",
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+ address = "Dubrovnik, Croatia",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2023.eacl-main.138",
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+ doi = "10.18653/v1/2023.eacl-main.138",
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+ pages = "1886--1894",
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+ abstract = "Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment. However, these large-scale models can suffer from inference speed and computation overhead. This study systematically explores learning language-agnostic sentence embeddings with lightweight models. We demonstrate that a thin-deep encoder can construct robust low-dimensional sentence embeddings for 109 languages. With our proposed distillation methods, we achieve further improvements by incorporating knowledge from a teacher model. Empirical results on Tatoeba, United Nations, and BUCC show the effectiveness of our lightweight models. We release our lightweight language-agnostic sentence embedding models LEALLA on TensorFlow Hub.",
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  }
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  ```