6 citations · 6 across the 1 of their papers we have counts for
6 papers
Mitigating Noisy Inputs for Question Answering
Denis Peskov, Joe Barrow, Pedro Rodriguez +2
Natural language processing systems are often downstream of unreliable inputs: machine translation, optical character recognition, or speech recognition. For instance, virtual assi…
Misleading Failures of Partial-input Baselines
Shi Feng, Eric Wallace, Jordan Boyd-Graber
Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only models for SNLI or question-only models for VQA). Whe…
Quizbowl: The Case for Incremental Question Answering
Pedro Rodriguez, Shi Feng, Mohit Iyyer +2
Scholastic trivia competitions test knowledge and intelligence through mastery of question answering. Modern question answering benchmarks are one variant of the Turing test. Speci…
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
Shi Feng, Jordan Boyd-Graber
Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudie…
Interpreting Neural Networks With Nearest Neighbors
Eric Wallace, Shi Feng, Jordan Boyd-Graber
Local model interpretation methods explain individual predictions by assigning an importance value to each input feature. This value is often determined by measuring the change in…
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering
Eric Wallace, Pedro Rodriguez, Shi Feng +2
Adversarial evaluation stress tests a model's understanding of natural language. While past approaches expose superficial patterns, the resulting adversarial examples are limited i…