3 papers
cs.CL2019
Evaluating Gender Bias in Machine Translation
Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer
We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets…
cs.CL2017
Crowdsourcing Question-Answer Meaning Representations
Julian Michael, Gabriel Stanovsky, Luheng He +2
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a cr…
cs.CL2016
Getting More Out Of Syntax with PropS
Gabriel Stanovsky, Jessica Ficler, Ido Dagan +1
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed express…