You can view my CV here (updated 3/1/18).
I am PhD candidate in Linguistics at New York University. As a highly interdisciplinary researcher, I specialize in semantics, syntax, and their interface, with neurolinguistics, computational linguistics and formal linguistic theory as my main methodologies. Theoretical linguistic insights form the bases for my hypotheses about grammatical representations in the mind and computer. I am affiliated with the NYU Neuroscience of Language Lab (NELLab; Liina Pylkkänen), and with the Machine Learning for Language (ML² Group; Sam Bowman).
My dissertation, entitled Representing Relationality: MEG Studies of Argument Structure is anticipated in May 2018. It will include 2-3 MEG experiments, and a chapter on classifying words as transitive-intransitive using only their orthographic forms.
I recently released the Multi-genre Natural Language Inference (MultiNLI) corpus, which is a crowd-sourced collection of sentence pairs (433k) annotated with textual entailment information for use in NLP and Machine Learning applications. It was the basis of shared task associated with the RepEval 2017 Workshop at EMNLP 2017 (Copenhagen); training and test data is available in full on Kaggle as part of an indefinitely open evaluation (for both matched and mismatched subsets). The manuscript describing the data and giving some baselines is available here; a manuscript detailing the results of the shared task is available here. We used MultiNLI as part of some exciting new work on evaluating latent parse trees that were arrived at by end-to-end deep architectures doing Natural Language Inference without reference to explicit parse trees at training (here). Both of these works will be presented at NAACL 2018 in New Orleans.
When not looking at pretty brains or coding, I do theoretical work on the syntax-semantics interface at the overlap of the Semantics Group, the Syntax Research Group, and the Morphology Reading Group, where I work on the semantics of inflectional morphology (mainly viewpoint aspect and its connection to adpositional meaning in Mandarin Chinese, and number and definiteness in American English), and investigate the effect of lexical “constants” on syntactic realization.
Brief Summary of Interests
- Syntax-Semantics Interface
- Experimental & Computational Approaches to Linguistics
- Brain Basis of Syntactic and Semantic Processing
- Argument Structure and Event Structure
- Syntactic Category
- Representations of Number
- Natural Language Understanding
- How NLP methods can aid in the investigation of meaning
- Creating Corpora for Natural Language Inference
- Evaluating syntactic representations induced from TreeRNNs that perform semantic tasks
- Semantics of Inflectional Morphology
- Number Interpretation, Marking, and Countability
- Prepositional and Verbal Aspect
- Bare Singulars and Weak Definites
- (Morpho)Syntax and Semantics of Mandarin Chinese