Jesse Dodge


2022

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Efficient Hierarchical Domain Adaptation for Pretrained Language Models
Alexandra Chronopoulou | Matthew Peters | Jesse Dodge
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information about the source of the text is rarely used during training. Transferring their knowledge to a target domain is typically done by continuing training in-domain. In this paper, we introduce a method to permit domain adaptation to many diverse domains using a computationally efficient adapter approach. Our method is based on the observation that textual domains are partially overlapping, and we represent domains as a hierarchical tree structure where each node in the tree is associated with a set of adapter weights. When combined with a frozen pretrained language model, this approach enables parameter sharing among related domains, while avoiding negative interference between unrelated ones. Experimental results with GPT-2 and a large fraction of the 100 most represented websites in C4 show across-the-board improvements in-domain. We additionally provide an inference time algorithm for a held-out domain and show that averaging over multiple paths through the tree enables further gains in generalization, while adding only a marginal cost to inference.

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Towards Reproducible Machine Learning Research in Natural Language Processing
Ana Lucic | Maurits Bleeker | Samarth Bhargav | Jessica Forde | Koustuv Sinha | Jesse Dodge | Sasha Luccioni | Robert Stojnic
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs.

2021

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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Jesse Dodge | Maarten Sap | Ana Marasović | William Agnew | Gabriel Ilharco | Dirk Groeneveld | Margaret Mitchell | Matt Gardner
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.

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Competency Problems: On Finding and Removing Artifacts in Language Data
Matt Gardner | William Merrill | Jesse Dodge | Matthew Peters | Alexis Ross | Sameer Singh | Noah A. Smith
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases. Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.

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Expected Validation Performance and Estimation of a Random Variable’s Maximum
Jesse Dodge | Suchin Gururangan | Dallas Card | Roy Schwartz | Noah A. Smith
Findings of the Association for Computational Linguistics: EMNLP 2021

Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.

2020

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The Right Tool for the Job: Matching Model and Instance Complexities
Roy Schwartz | Gabriel Stanovsky | Swabha Swayamdipta | Jesse Dodge | Noah A. Smith
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference budget, we propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) “exit” from neural network calculations for simple instances, and late (and accurate) exit for hard instances. To achieve this, we add classifiers to different layers of BERT and use their calibrated confidence scores to make early exit decisions. We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks. Our method presents a favorable speed/accuracy tradeoff in almost all cases, producing models which are up to five times faster than the state of the art, while preserving their accuracy. Our method also requires almost no additional training resources (in either time or parameters) compared to the baseline BERT model. Finally, our method alleviates the need for costly retraining of multiple models at different levels of efficiency; we allow users to control the inference speed/accuracy tradeoff using a single trained model, by setting a single variable at inference time. We publicly release our code.

2019

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RNN Architecture Learning with Sparse Regularization
Jesse Dodge | Roy Schwartz | Hao Peng | Noah A. Smith
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to rational RNNs (Peng et al., 2018), a family of models that is closely connected to weighted finite-state automata (WFSAs). We take advantage of rational RNNs’ natural grouping of the weights, so the group lasso penalty directly removes WFSA states, substantially reducing the number of parameters in the model. Our experiments on a number of sentiment analysis datasets, using both GloVe and BERT embeddings, show that our approach learns neural structures which have fewer parameters without sacrificing performance relative to parameter-rich baselines. Our method also highlights the interpretable properties of rational RNNs. We show that sparsifying such models makes them easier to visualize, and we present models that rely exclusively on as few as three WFSAs after pruning more than 90% of the weights. We publicly release our code.

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Show Your Work: Improved Reporting of Experimental Results
Jesse Dodge | Suchin Gururangan | Dallas Card | Roy Schwartz | Noah A. Smith
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate conclusions about which model performs best. We argue for reporting additional details, especially performance on validation data obtained during model development. We present a novel technique for doing so: expected validation performance of the best-found model as a function of computation budget (i.e., the number of hyperparameter search trials or the overall training time). Using our approach, we find multiple recent model comparisons where authors would have reached a different conclusion if they had used more (or less) computation. Our approach also allows us to estimate the amount of computation required to obtain a given accuracy; applying it to several recently published results yields massive variation across papers, from hours to weeks. We conclude with a set of best practices for reporting experimental results which allow for robust future comparisons, and provide code to allow researchers to use our technique.

2016

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Key-Value Memory Networks for Directly Reading Documents
Alexander Miller | Adam Fisch | Jesse Dodge | Amir-Hossein Karimi | Antoine Bordes | Jason Weston
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Retrofitting Word Vectors to Semantic Lexicons
Manaal Faruqui | Jesse Dodge | Sujay Kumar Jauhar | Chris Dyer | Eduard Hovy | Noah A. Smith
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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CMU: Arc-Factored, Discriminative Semantic Dependency Parsing
Sam Thomson | Brendan O’Connor | Jeffrey Flanigan | David Bamman | Jesse Dodge | Swabha Swayamdipta | Nathan Schneider | Chris Dyer | Noah A. Smith
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Context-dependent Semantic Parsing for Time Expressions
Kenton Lee | Yoav Artzi | Jesse Dodge | Luke Zettlemoyer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Midge: Generating Image Descriptions From Computer Vision Detections
Margaret Mitchell | Jesse Dodge | Amit Goyal | Kota Yamaguchi | Karl Stratos | Xufeng Han | Alyssa Mensch | Alex Berg | Tamara Berg | Hal Daumé III
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Detecting Visual Text
Jesse Dodge | Amit Goyal | Xufeng Han | Alyssa Mensch | Margaret Mitchell | Karl Stratos | Kota Yamaguchi | Yejin Choi | Hal Daumé III | Alex Berg | Tamara Berg
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies