139 citations · 237 across the 27 of their papers we have counts for
13 papers · 1 filter
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia +13
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pre…
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu +3
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-o…
Predicting the Argumenthood of English Prepositional Phrases
Najoung Kim, Kyle Rawlins, Benjamin Van Durme +1
Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such a…
Lexicosyntactic Inference in Neural Models
Aaron Steven White, Rachel Rudinger, Kyle Rawlins +1
We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event…
Efficient Online Scalar Annotation with Bounded Support
Keisuke Sakaguchi, Benjamin Van Durme
We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (an…
Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction
Hongyuan Mei, Sheng Zhang, Kevin Duh +1
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, call…