swabha swayamdipta

swabha@cs.cmu.edu
185 Stevens Way, Box 352350, Seattle WA
me

I am a PhD student in the Language Technologies Institute at Carnegie Mellon University. My advisors are Noah Smith and Chris Dyer. My research interests are primarily in structured prediction for natural language, with a focus on broad-coverage semantic parsing. In particular, I am interested in exploring new ways of incorporating syntactic cues and structural inductive biases to predict semantic structures for data across multiple domains, genres and languages. In addition to predicting semantic structures for language with high accuracy, I’m also interested in exploring how these structures can be utilized for downstream applications such as question answering.

In 2013, I graduated from a Masters program at Columbia University, where I was advised by Owen Rambow. During this period, I worked on detection of influence and power in online social media.

Since Aug 2015, I have relocated to Seattle, and am currently a visiting student at CSE in University of Washington, Seattle.

publications

  • Polyglot Semantic Role Labeling.
    Phoebe Mulcaire, Swabha Swayamdipta, Noah A. Smith.
    arXiv preprint, arXiv:1805.11598 [cs.CL] . To be presented at ACL 2018.
  • Learning Joint Semantic Parsers from Disjoint Data.
    Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith.
    arXiv preprint, arXiv:1804.05990 [cs.CL] . To be presented at NAACL 2018.
  • Annotation Artifacts in Natural Language Inference Data.
    Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, Noah A. Smith.
    arXiv preprint, arXiv:1803.02324 [cs.CL] . To be presented at NAACL 2018.
  • Multi-Mention Learning for Reading Comprehension with Neural Cascades.
    Swabha Swayamdipta, Ankur P. Parikh and Tom Kwaitkowski.
    Proceedings of International Conference on Learning Representations 2018. [Poster]
  • Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold.
    Swabha Swayamdipta, Sam Thomson, Chris Dyer and Noah A. Smith.
    arXiv preprint, arXiv:1706.09528 [cs.CL] . [Poster] [Code]
  • Multi-task Learning for Incremental Parsing using Stack LSTMs.
    Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer and Noah A. Smith.
    Workshop for Women in Machine Learning, Neural Information Processing Systems (NIPS 2016). [Poster]
  • Greedy, Joint Syntactic and Semantic Parsing with Stack LSTMs.
    Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer and Noah A. Smith.
    SIGNLL Conference on Computational Natural Language Learning (CoNLL 2016). [Code] [Talk]
  • A Dependency Parser for Tweets.
    Lingpeng Kong, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer and Noah A. Smith.
    Conference on Empirical Methods in Natural Language Processing (EMNLP 2014).
  • CMU: Arc-Factored, Discriminative Semantic Dependency Parsing.
    Sam Thomson, David Bamman, Jesse Dodge, Swabha Swayamdipta, Nathan Schneider, Chris Dyer and Noah A. Smith.
    International Workshop on Semantic Evaluation (SemEval 2014).
  • The CMU Machine Translation Systems.
    Austin Matthews, Chris Dyer, Alon Lavie, Greg Hanneman, Waleed Ammar, Archna Bhatia, Swabha Swayamdipta, Eva Schlinger and Yulia Tsvetkov.
    Workshop on Machine Translation (WMT 2014).
  • The Pursuit of Power and its Manifestation in Written Dialog.
    Swabha Swayamdipta and Owen Rambow.
    IEEE International Conference of Semantic Computing (ICSC 2012).
  • teaching

  • I TAed for Undergrad NLP (11-411) taught by Chris Dyer, Alon Lavie and Shomir Wilson, in the Spring of 2015.
  • With Sam Thomson, I gave a lecture on the Chu-Liu-Edmonds algorithm for Algorithms for NLP in the Fall of 2014.
  • others

  • I am a strong advocate for Beamer presentations. Despite a steep learning curve, it is immensely rewarding to see the end product.
  • I've sworn to blog about talks, conferences and topics that are worth spreading the word about.