8 citations · 10 across the 4 of their papers we have counts for
4 papers
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler +3
There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess…
Richer Countries and Richer Representations
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
We examine whether some countries are more richly represented in embedding space than others. We find that countries whose names occur with low frequency in training corpora are mo…
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card +1
Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore). Here, we uncover systematic ways in which word similarities e…
Frequency-based Distortions in Contextualized Word Embeddings
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
How does word frequency in pre-training data affect the behavior of similarity metrics in contextualized BERT embeddings? Are there systematic ways in which some word relationships…