Tommi S. Jaakkola, Ph.D.
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society

MIT Computer Science and Artificial Intelligence Laboratory
Stata Center, Bldg 32-G470
Cambridge, MA 02139

tommi at csail dot mit dot edu

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You can view all the papers in reverse chronological order, sets of papers related to broad categories such as machine learning, natural language processing, chemistry, computational biology, or physics, or papers in more specific areas including inference, semi-supervised learning , information retrieval, or reinforcement learning.

NLP papers

  • S. Chang, Y. Zhang, M. Yu, and T. Jaakkola.
    Invariant rationalization.
    In International Conference on Machine Learning (ICML), 2020.
    [link]
  • T. Shen, J. Mueller, R. Barzilay, and T. Jaakkola.
    Educating text autoencoders: Latent representation guidance via denoising.
    In International Conference on Machine Learning (ICML), 2020.
    [link]
  • S. Chang, Y. Zhang, M. Yu, and T. Jaakkola.
    A game theoretic approach to class-wise selective rationalization.
    In Neural Information Processing Systems (NeurIPS), 2019.
    [pdf]
  • M. Yu, S. Chang, Y. Zhang, and T. Jaakkola.
    Rethinking cooperative rationalization: Introspective extraction and complement control.
    In Empirical Methods in Natural Language Processing (EMNLP), 2019.
    [pdf]
  • K. Narasimhan, R. Barzilay, and T. Jaakkola.
    Grounding language for transfer in deep reinforcement learning.
    Journal of Artificial Intelligence Research, 63:849--874, 2018.
    [pdf]
  • D. Alvarez Melis and T. Jaakkola.
    Gromov-wasserstein alignment of word embedding spaces.
    In Empirical Methods in Natural Language Processing (EMNLP), 2018.
    [pdf]
  • T. Shen, T., R. Barzilay, and T. Jaakkola.
    Style transfer from non-parallel text by cross-alignment.
    In Advances in Neural Information Processing Systems (NIPS), 2017.
    [link]
  • Y. Zhang, R. Barzilay, and T. Jaakkola.
    Aspect-augmented adversarial networks for domain adaptation.
    Transactions of the Association for Computational Linguistics (TACL), 2017.
    [pdf]
  • D. Alvarez Melis and T. Jaakkola.
    A causal framework for explaining the predictions of black-box sequence-to-sequence models.
    In Empirical Methods in Natural Language Processing (EMNLP), 2017.
    [pdf]
  • T. Lei, R. Barzilay, and T. Jaakkola.
    Rationalizing neural predictions.
    In Empirical Methods in Natural Language Processing (EMNLP), 2016.
    [pdf]
  • Y. Gu, R. Barzilay, and T. Jaakkola.
    Food adulteration detection using neural networks.
    In Empirical Methods in Natural Language Processing (EMNLP), 2016.
  • T. Hashimoto, D. Alvarez-Melis, and T. Jaakkola.
    Word embeddings as metric recovery in semantic spaces.
    Transactions of the Association for Computational Linguistics (TACL), 4, 2016.
    [pdf]
  • Y. Zhang, D. Gaddy, R. Barzilay, and T. Jaakkola.
    Ten pairs to tag -- multilingual pos tagging via coarse mapping between embeddings.
    In The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016.
    [pdf]
  • T. Lei, H. Joshi, R. Barzilay, T. Jaakkola, K. Tymoshenko, A. Moschitti, and L. Marquez.
    Semi-supervised question retrieval with gated convolutions.
    In The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016.
    [pdf]
  • T. Hashimoto, D. Alvarez-Melis, and T. Jaakkola.
    Word, graph and manifold embedding from markov processes.
    In arXiv:1509.05808, 2015.
    [link]
  • T. Lei, R. Barzilay, and T. Jaakkola.
    Molding {CNN}s for text: Non-linear, non-consecutive convolutions.
    In Empirical Methods in Natural Language Processing, 2015.
    [pdf] [link]
  • K. Narasimhan, R. Barzilay, and T. Jaakkola.
    An unsupervised method for uncovering morphological chains.
    Transactions of the Association for Computational Linguistics, 3:157--167, 2015.
    [pdf] [link]
  • Y. Zhang, T. Lei, R. Barzilay, and T. Jaakkola.
    Greed is good if randomized: New inference for dependency parsing.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
    [pdf]
  • T. Lei, Y. Xin, Y. Zhang, R. Barzilay, and T. Jaakkola.
    Low-rank tensors for scoring dependency structures.
    In Association for Computational Linguistics, 2014.
    [pdf]
  • Y. Zhang, T. Lei, R. Barzilay, T. Jaakkola, and A. Globerson.
    Steps to excellence: Simple inference with refined scoring of dependency trees.
    In Association for Computational Linguistics, 2014.
    [pdf]
  • A. Rush, D. Sontag, M. Collins, and T. Jaakkola.
    On dual decomposition and linear programming relaxations for natural language processing.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
    [pdf]
  • T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag.
    Dual decomposition for parsing with non-projective head automata.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
    [pdf]