Vered Shwartz

“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models

2020

Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models. We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be …

Breaking NLI Systems with Sentences that Require Simple Lexical Inferences

2018

We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that …

Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution

2019

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to withindocument entity coreference, with rather little attention to the other variants. …

Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition

2019 · Transactions of the Association for Computational Linguistics

Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a …