William Huang


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Does Putting a Linguist in the Loop Improve NLU Data Collection?
Alicia Parrish | William Huang | Omar Agha | Soo-Hwan Lee | Nikita Nangia | Alexia Warstadt | Karmanya Aggarwal | Emily Allaway | Tal Linzen | Samuel R. Bowman
Findings of the Association for Computational Linguistics: EMNLP 2021

Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work ‘in the loop’ during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.

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Comparing Test Sets with Item Response Theory
Clara Vania | Phu Mon Htut | William Huang | Dhara Mungra | Richard Yuanzhe Pang | Jason Phang | Haokun Liu | Kyunghyun Cho | Samuel R. Bowman
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)

Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.

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Types of Out-of-Distribution Texts and How to Detect Them
Udit Arora | William Huang | He He
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of the distribution shifts of OOD examples and how to best detect them. We categorize these examples as exhibiting a background shift or semantic shift, and find that the two major approaches to OOD detection, calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data. Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings and perform worse in semantic shift settings. In addition, we find that both methods generally fail to detect examples from challenge data, indicating that these examples constitute a different type of OOD data. Overall, while the categorization we apply explains many of the differences between the two methods, our results call for a more explicit definition of OOD to create better benchmarks and build detectors that can target the type of OOD data expected at test time.


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Precise Task Formalization Matters in Winograd Schema Evaluations
Haokun Liu | William Huang | Dhara Mungra | Samuel R. Bowman
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Performance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89% on the SuperGLUE leaderboard, with relatively little corroborating evidence of a correspondingly large improvement in reasoning ability. We hypothesize that much of this improvement comes from recent changes in task formalization—the combination of input specification, loss function, and reuse of pretrained parameters—by users of the dataset, rather than improvements in the pretrained model’s reasoning ability. We perform an ablation on two Winograd Schema datasets that interpolates between the formalizations used before and after this surge, and find (i) framing the task as multiple choice improves performance dramatically and (ii)several additional techniques, including the reuse of a pretrained language modeling head, can mitigate the model’s extreme sensitivity to hyperparameters. We urge future benchmark creators to impose additional structure to minimize the impact of formalization decisions on reported results.

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Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data
William Huang | Haokun Liu | Samuel R. Bowman
Proceedings of the First Workshop on Insights from Negative Results in NLP

A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks—datasets collected from crowdworkers to create an evaluation task—while still failing on out-of-domain examples for the same task. Recent work has explored the use of counterfactually-augmented data—data built by minimally editing a set of seed examples to yield counterfactual labels—to augment training data associated with these benchmarks and build more robust classifiers that generalize better. However, Khashabi et al. (2020) find that this type of augmentation yields little benefit on reading comprehension tasks when controlling for dataset size and cost of collection. We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactually-augmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples. Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.