24 citations · 32 across the 13 of their papers we have counts for
17 papers · 1 filter
Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords
Taelin Karidi, Yichu Zhou, Nathan Schneider +2
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond…
X-FACT: A New Benchmark Dataset for Multilingual Fact Checking
Ashim Gupta, Vivek Srikumar
In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short…
DirectProbe: Studying Representations without Classifiers
Yichu Zhou, Vivek Srikumar
Understanding how linguistic structures are encoded in contextualized embedding could help explain their impressive performance across NLP@. Existing approaches for probing them us…
Incorporating External Knowledge to Enhance Tabular Reasoning
J. Neeraja, Vivek Gupta, Vivek Srikumar
Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study…
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
Archit Rathore, Sunipa Dev, Jeff M. Phillips +6
Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and at…
BERT & Family Eat Word Salad: Experiments with Text Understanding
Ashim Gupta, Giorgi Kvernadze, Vivek Srikumar
In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define s…