Publications

Consistent Autoformalization for Constructing Mathematical Libraries

Published in Empirical Methods in Natural Language Processing (EMNLP), 2024

Oral Presentation. A long paper studies autoformalization in a mathematical library setting.

Recommended citation: Lan Zhang, Xin Quan, and Andre Freitas. 2024. Consistent Autoformalization for Constructing Mathematical Libraries. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4020–4033, Miami, Florida, USA. Association for Computational Linguistics. https://aclanthology.org/2024.emnlp-main.233

Multi-Operational Mathematical Derivations in Latent Space

Published in North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2024

Poster.

Recommended citation: Marco Valentino, Jordan Meadows, Lan Zhang, and Andre Freitas. 2024. Multi-Operational Mathematical Derivations in Latent Space. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1446–1458, Mexico City, Mexico. Association for Computational Linguistics. https://aclanthology.org/2024.naacl-long.80

On the Effect of Isotropy on VAE Representations of Text

Published in Association for Computational Linguistics (ACL), 2022

Oral Presentation. A short paper studies the effect of injecting isotropy into the representation of VAEs.

Recommended citation: Lan Zhang, Wray Buntine, and Ehsan Shareghi. 2022. On the Effect of Isotropy on VAE Representations of Text. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 694–701, Dublin, Ireland. Association for Computational Linguistics. https://aclanthology.org/2022.acl-short.78

Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

Published in Workshop on Representation Learning for NLP (RepL4NLP), 2021

Poster. A long paper studies disentanglement for text.

Recommended citation: Lan Zhang, Victor Prokhorov, and Ehsan Shareghi. 2021. Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 128–140, Online. Association for Computational Linguistics. https://aclanthology.org/2021.repl4nlp-1.14