Selected Papers (see my semantic scholar profile or g-scholar profile for the full list)

  • Many Languages, One Parser
    Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, Noah A. Smith TACL 2016

    We train one model for dependency parsing and use it to parse competitively in several languages. The parsing model uses multilingual word clusters and multilingual word em-beddings alongside learned and specified ty-pological information, enabling generalization based on linguistic universals and typologi-cal similarities. Our model can also incorporate language-specific features (e.g., fine POS tags), enabling still letting the parser to learn language-specific behaviors. Our parser compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small tree-bank, or no treebank for training. Less

  • Conditional Random Field Autoencoders for Unsupervised Structured Prediction
    Waleed Ammar, Chris Dyer, Noah A. Smith NIPS 2014

    We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observed data using a feature-rich conditional random field (CRF). Then a reconstruction of the input is (re)generated, conditional on the latent structure , using a generative model which factorizes similarly to the CRF. The autoen-coder formulation enables efficient exact inference without resorting to unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate connections to traditional autoencoders, posterior regularization, and multi-view learning. We then show competitive results with instantiations of the framework for two canonical tasks in natural language processing: part-of-speech induction and bitext word alignment, and show that training the proposed model can be substantially more efficient than a comparable feature-rich baseline. Less