Jin, L., Doshi-Velez, F., Miller, T., Schuler, W., & Schwartz, L. (2020). Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 2721-2731). (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018). Association for Computational Linguistics.

Jin, L., Doshi-Velez, F., Miller, T., Schuler, W., & Schwartz, L. (2020). Unsupervised learning of PCFGs with normalizing flow. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 2442-2452). (ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference). Association for Computational Linguistics (ACL).

Chen, E., & Schwartz, L. (2019). A morphological analyzer for St. Lawrence Island / Central Siberian Yupik. In H. Isahara, B. Maegaard, S. Piperidis, C. Cieri, T. Declerck, K. Hasida, H. Mazo, K. Choukri, S. Goggi, J. Mariani, A. Moreno, N. Calzolari, J. Odijk, & T. Tokunaga (Eds.), LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 2623-2630). (LREC 2018 - 11th International Conference on Language Resources and Evaluation). European Language Resources Association (ELRA).

Schwartz, L. (2018). The history and promise of machine translation. In American Translators Association Scholarly Monograph Series (Vol. 18, pp. 161-190). John Benjamins Publishing Company. https://doi.org/10.1075/ata.18.08sch

Wei, R., Schwartz, L., & Adve, V. (2018). DLVM: A modern compiler infrastructure for deep learning systems. Paper presented at 6th International Conference on Learning Representations, ICLR 2018, Vancouver, Canada.

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