Pedro Rodriguez


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Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?
Pedro Rodriguez | Joe Barrow | Alexander Miserlis Hoyle | John P. Lalor | Robin Jia | Jordan Boyd-Graber
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.


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Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity
Pedro Rodriguez | Paul Crook | Seungwhan Moon | Zhiguang Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user’s pre-existing knowledge. Assuming a correlation between engagement and user responses such as “liking” messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user’s prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.


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Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering
Eric Wallace | Pedro Rodriguez | Shi Feng | Ikuya Yamada | Jordan Boyd-Graber
Transactions of the Association for Computational Linguistics, Volume 7

Adversarial evaluation stress-tests a model’s understanding of natural language. Because past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human–computer matches: Although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.


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Pathologies of Neural Models Make Interpretations Difficult
Shi Feng | Eric Wallace | Alvin Grissom II | Mohit Iyyer | Pedro Rodriguez | Jordan Boyd-Graber
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

One way to interpret neural model predictions is to highlight the most important input features—for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word’s importance is determined by either input perturbation—measuring the decrease in model confidence when that word is removed—or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction, without accuracy loss on regular examples.