8 citations · 45 across the 15 of their papers we have counts for
7 papers · 1 filter
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?
Yang Trista Cao, Kyle Seelman, Kyungjun Lee +1
In visual question answering (VQA), a machine must answer a question given an associated image. Recently, accessibility researchers have explored whether VQA can be deployed in a r…
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Yang Trista Cao, Anna Sotnikova, Hal Daumé +2
NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Be…
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…
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber +1
Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge…
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
Tianze Shi, Chen Zhao, Jordan Boyd-Graber +2
Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the u…
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé +1
We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyz…
Meta-Learning for Few-Shot NMT Adaptation
Amr Sharaf, Hany Hassan, Hal Daumé
We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adap…