Emma Strubell


2024

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Challenges in End-to-End Policy Extraction from Climate Action Plans
Nupoor Gandhi | Tom Corringham | Emma Strubell
Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)

Gray policy literature such as climate action plans (CAPs) provide an information-rich resource with potential to inform analysis and decision-making. However, these corpora are currently underutilized due to the substantial manual effort and expertise required to sift through long and detailed documents. Automatically structuring relevant information using information extraction (IE) would be useful for assisting policy scientists in synthesizing vast gray policy corpora to identify relevant entities, concepts and themes. LLMs have demonstrated strong performance on IE tasks in the few-shot setting, but it is unclear whether these gains transfer to gray policy literature which differs significantly to traditional benchmark datasets in several aspects, such as format of information content, length of documents, and inconsistency of document structure. We perform a case study on end-to-end IE with California CAPs, inspecting the performance of state-of-the-art tools for: (1) extracting content from CAPs into structured markup segments; (2) few-shot IE with LLMs; and (3) the utility of extracted entities for downstream analyses. We identify challenges at several points of the end-to-end IE pipeline for CAPs, and we provide recommendations for open problems centered around representing rich non-textual elements, document structure, flexible annotation schemes, and global information. Tackling these challenges would make it possible to realize the potential of LLMs for IE with gray policy literature.

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AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
Li Lucy | Suchin Gururangan | Luca Soldaini | Emma Strubell | David Bamman | Lauren Klein | Jesse Dodge
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Large language models’ (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten “quality” and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. Overall, we hope that our work will encourage a new line of research on pretraining data curation practices and its social implications.

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Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini | Rodney Kinney | Akshita Bhagia | Dustin Schwenk | David Atkinson | Russell Authur | Ben Bogin | Khyathi Chandu | Jennifer Dumas | Yanai Elazar | Valentin Hofmann | Ananya Jha | Sachin Kumar | Li Lucy | Xinxi Lyu | Nathan Lambert | Ian Magnusson | Jacob Morrison | Niklas Muennighoff | Aakanksha Naik | Crystal Nam | Matthew Peters | Abhilasha Ravichander | Kyle Richardson | Zejiang Shen | Emma Strubell | Nishant Subramani | Oyvind Tafjord | Evan Walsh | Luke Zettlemoyer | Noah Smith | Hannaneh Hajishirzi | Iz Beltagy | Dirk Groeneveld | Jesse Dodge | Kyle Lo
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation.

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OLMo: Accelerating the Science of Language Models
Dirk Groeneveld | Iz Beltagy | Evan Walsh | Akshita Bhagia | Rodney Kinney | Oyvind Tafjord | Ananya Jha | Hamish Ivison | Ian Magnusson | Yizhong Wang | Shane Arora | David Atkinson | Russell Authur | Khyathi Chandu | Arman Cohan | Jennifer Dumas | Yanai Elazar | Yuling Gu | Jack Hessel | Tushar Khot | William Merrill | Jacob Morrison | Niklas Muennighoff | Aakanksha Naik | Crystal Nam | Matthew Peters | Valentina Pyatkin | Abhilasha Ravichander | Dustin Schwenk | Saurabh Shah | William Smith | Emma Strubell | Nishant Subramani | Mitchell Wortsman | Pradeep Dasigi | Nathan Lambert | Kyle Richardson | Luke Zettlemoyer | Jesse Dodge | Kyle Lo | Luca Soldaini | Noah Smith | Hannaneh Hajishirzi
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.

2023

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Energy and Carbon Considerations of Fine-Tuning BERT
Xiaorong Wang | Clara Na | Emma Strubell | Sorelle Friedler | Sasha Luccioni
Findings of the Association for Computational Linguistics: EMNLP 2023

Despite the popularity of the pre-train then fine-tune paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused on language model pre-training. Although a single pre-training run draws substantially more energy than fine-tuning, fine-tuning is performed more frequently by many more individual actors, and thus must be accounted for when considering the energy and carbon footprint of NLP. In order to better characterize the role of fine-tuning in the landscape of energy and carbon emissions in NLP, we perform a careful empirical study of the computational costs of fine-tuning across tasks, datasets, hardware infrastructure and measurement modalities. Our experimental results allow us to place fine-tuning energy and carbon costs into perspective with respect to pre-training and inference, and outline recommendations to NLP researchers and practitioners who wish to improve their fine-tuning energy efficiency.

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Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training
Zhisong Zhang | Emma Strubell | Eduard Hovy
Findings of the Association for Computational Linguistics: EMNLP 2023

In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which reduces labeling costs for structured outputs by selecting only the most informative sub-structures for annotation. We also utilize self-training to incorporate the current model’s automatic predictions as pseudo-labels for un-annotated sub-structures. A key challenge in effectively combining partial annotation with self-training to reduce annotation cost is determining which sub-structures to select to label. To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model’s capability. In evaluations spanning four structured prediction tasks, we show that our combination of partial annotation and self-training using an adaptive selection ratio reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.

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Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso | Ji-Ung Lee | Tianchu Ji | Betty van Aken | Qingqing Cao | Manuel R. Ciosici | Michael Hassid | Kenneth Heafield | Sara Hooker | Colin Raffel | Pedro H. Martins | André F. T. Martins | Jessica Zosa Forde | Peter Milder | Edwin Simpson | Noam Slonim | Jesse Dodge | Emma Strubell | Niranjan Balasubramanian | Leon Derczynski | Iryna Gurevych | Roy Schwartz
Transactions of the Association for Computational Linguistics, Volume 11

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

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The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
Jared Fernandez | Jacob Kahn | Clara Na | Yonatan Bisk | Emma Strubell
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Increased focus on the computational efficiency of systems in natural language processing has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. However, the resulting increases in computational throughput and reductions in floating point operations have not directly translated to improvements in wall-clock inference latency. We demonstrate that these discrepancies can be largely attributed to bottlenecks introduced by deep learning frameworks. We denote this phenomena as the framework tax, and observe that the disparity is growing as hardware speed increases over time. In this work, we examine this phenomena through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency. Based on our findings, we provide actionable recommendations to researchers and practitioners aimed at narrowing the gap between efficient NLP model research and practice.

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Understanding the Effect of Model Compression on Social Bias in Large Language Models
Gustavo Gonçalves | Emma Strubell
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text. Without intervention, these social biases persist in the model’s predictions in downstream tasks, leading to representational harm. Many strategies have been proposed to mitigate the effects of inappropriate social biases learned during pretraining. Simultaneously, methods for model compression have become increasingly popular to reduce the computational burden of LLMs. Despite the popularity and need for both approaches, little work has been done to explore the interplay between these two. We perform a carefully controlled study of the impact of model compression via quantization and knowledge distillation on measures of social bias in LLMs. Longer pretraining and larger models led to higher social bias, and quantization showed a regularizer effect with its best trade-off around 20% of the original pretraining time.

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DSI++: Updating Transformer Memory with New Documents
Sanket Vaibhav Mehta | Jai Gupta | Yi Tay | Mostafa Dehghani | Vinh Q. Tran | Jinfeng Rao | Marc Najork | Emma Strubell | Donald Metzler
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the solid performance of DSI models, successfully deploying them in scenarios where document corpora change with time is an open problem. In this work, we introduce DSI++, a continual learning challenge for DSI with the goal of continuously indexing new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviates forgetting, so we explicitly optimize for flatter loss basins and show that the model stably memorizes more documents (+12%). Next, we introduce a parametric memory to generate pseudo-queries for documents and supplement them during incremental indexing to prevent forgetting for the retrieval task. Extensive experiments on a novel continual indexing benchmark based on Natural Questions demonstrate that our proposed solution mitigates the forgetting in DSI++ by a significant margin and improves the average Hits@10 by +21.1% over competitive baselines.

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To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
Sireesh Gururaja | Amanda Bertsch | Clara Na | David Widder | Emma Strubell
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity. Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure. We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time. We conclude by discussing shared visions, concerns, and hopes for the future of NLP. We hope that this study of our field’s past and present can prompt informed discussion of our community’s implicit norms and more deliberate action to consciously shape the future.

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Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution
Nupoor Gandhi | Anjalie Field | Emma Strubell
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14% improvement in average F1 without increasing annotator time.

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To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Dheeru Dua | Emma Strubell | Sameer Singh | Pat Verga
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on general-purpose domains like Wikipedia. While some work has been investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (i.e., retriever or reader) rather than an end-to-end system. This work proposes a more realistic end-to-end domain shift evaluation setting covering five diverse domains. We not only find that end-to-end models fail to generalize but that high retrieval scores often still yield poor answer prediction accuracy. To address these failures, we investigate several interventions, in the form of data augmentations, for improving model adaption and use our evaluation set to elucidate the relationship between the efficacy of an intervention scheme and the particular type of dataset shifts we consider. We propose a generalizability test that estimates the type of shift in a target dataset without training a model in the target domain and that the type of shift is predictive of which data augmentation schemes will be effective for domain adaption. Overall, we find that these interventions increase end-to-end performance by up to ~24 points.

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On the Interactions of Structural Constraints and Data Resources for Structured Prediction
Zhisong Zhang | Emma Strubell | Eduard Hovy
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

2022

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Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying
Zhisong Zhang | Emma Strubell | Eduard Hovy
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In this work, we investigate transfer learning from semantic role labeling (SRL) to event argument extraction (EAE), considering their similar argument structures. We view the extraction task as a role querying problem, unifying various methods into a single framework. There are key discrepancies on role labels and distant arguments between semantic role and event argument annotations. To mitigate these discrepancies, we specify natural language-like queries to tackle the label mismatch problem and devise argument augmentation to recover distant arguments. We show that SRL annotations can serve as a valuable resource for EAE, and a template-based slot querying strategy is especially effective for facilitating the transfer. In extensive evaluations on two English EAE benchmarks, our proposed model obtains impressive zero-shot results by leveraging SRL annotations, reaching nearly 80% of the fullysupervised scores. It further provides benefits in low-resource cases, where few EAE annotations are available. Moreover, we show that our approach generalizes to cross-domain and multilingual scenarios.

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A Survey of Active Learning for Natural Language Processing
Zhisong Zhang | Emma Strubell | Eduard Hovy
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.

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Bridging Fairness and Environmental Sustainability in Natural Language Processing
Marius Hessenthaler | Emma Strubell | Dirk Hovy | Anne Lauscher
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of research on the interplay between the two fields. This lacuna is highly problematic, since there is increasing evidence that an exclusive focus on fairness can actually hinder environmental sustainability, and vice versa. In this work, we shed light on this crucial intersection in NLP by (1) investigating the efficiency of current fairness approaches through surveying example methods for reducing unfair stereotypical bias from the literature, and (2) evaluating a common technique to reduce energy consumption (and thus environmental impact) of English NLP models, knowledge distillation (KD), for its impact on fairness. In this case study, we evaluate the effect of important KD factors, including layer and dimensionality reduction, with respect to: (a) performance on the distillation task (natural language inference and semantic similarity prediction), and (b) multiple measures and dimensions of stereotypical bias (e.g., gender bias measured via the Word Embedding Association Test). Our results lead us to clarify current assumptions regarding the effect of KD on unfair bias: contrary to other findings, we show that KD can actually decrease model fairness.

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Evaluating Gender Bias Transfer from Film Data
Amanda Bertsch | Ashley Oh | Sanika Natu | Swetha Gangu | Alan W. Black | Emma Strubell
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Films are a rich source of data for natural language processing. OpenSubtitles (Lison and Tiedemann, 2016) is a popular movie script dataset, used for training models for tasks such as machine translation and dialogue generation. However, movies often contain biases that reflect society at the time, and these biases may be introduced during pre-training and influence downstream models. We perform sentiment analysis on template infilling (Kurita et al., 2019) and the Sentence Embedding Association Test (May et al., 2019) to measure how BERT-based language models change after continued pre-training on OpenSubtitles. We consider gender bias as a primary motivating case for this analysis, while also measuring other social biases such as disability. We show that sentiment analysis on template infilling is not an effective measure of bias due to the rarity of disability and gender identifying tokens in the movie dialogue. We extend our analysis to a longitudinal study of bias in film dialogue over the last 110 years and find that continued pre-training on OpenSubtitles encodes additional bias into BERT. We show that BERT learns associations that reflect the biases and representation of each film era, suggesting that additional care must be taken when using historical data.

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Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Clara Na | Sanket Vaibhav Mehta | Emma Strubell
Findings of the Association for Computational Linguistics: EMNLP 2022

Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP. Inspired by prior works suggesting a connection between simpler, more generalizable models and those that lie within wider loss basins, we hypothesize that optimizing for flat minima should lead to simpler parameterizations and thus more compressible models. We propose to combine sharpness-aware minimization (SAM) with various task-specific model compression methods, including iterative magnitude pruning (IMP), structured pruning with a distillation objective, and post-training dynamic quantization. Empirically, we show that optimizing for flatter minima consistently leads to greater compressibility of parameters compared to vanilla Adam when fine-tuning BERT models, with little to no loss in accuracy on the GLUE text classification and SQuAD question answering benchmarks. Moreover, SAM finds superior winning tickets during IMP that 1) are amenable to vanilla Adam optimization, and 2) transfer more effectively across tasks.

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Improving Compositional Generalization with Self-Training for Data-to-Text Generation
Sanket Vaibhav Mehta | Jinfeng Rao | Yi Tay | Mihir Kale | Ankur Parikh | Emma Strubell
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs. In this work, we systematically study the compositional generalization of the state-of-the-art T5 models in few-shot data-to-text tasks. We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model’s generalization capability. To further improve the model’s performance, we propose an approach based on self-training using fine-tuned BLEURT for pseudo-response selection. On the commonly-used SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46%+ and reduces the slot error rates by 73%+ over the strong T5 baselines in few-shot settings.

2021

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Comparing Span Extraction Methods for Semantic Role Labeling
Zhisong Zhang | Emma Strubell | Eduard Hovy
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear. With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings. However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished. We also experiment in cross-genre and cross-lingual settings and find similar trends. We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods.

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On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling
Zhisong Zhang | Emma Strubell | Eduard Hovy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant. Cross-lingual learning can improve performance in this setting by transferring knowledge from high-resource languages to low-resource ones. Moreover, we hypothesize that annotations of syntactic dependencies can be leveraged to further facilitate cross-lingual transfer. In this work, we perform an empirical exploration of the helpfulness of syntactic supervision for crosslingual SRL within a simple multitask learning scheme. With comprehensive evaluations across ten languages (in addition to English) and three SRL benchmark datasets, including both dependency- and span-based SRL, we show the effectiveness of syntactic supervision in low-resource scenarios.

2020

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Proceedings of the 5th Workshop on Representation Learning for NLP
Spandana Gella | Johannes Welbl | Marek Rei | Fabio Petroni | Patrick Lewis | Emma Strubell | Minjoon Seo | Hannaneh Hajishirzi
Proceedings of the 5th Workshop on Representation Learning for NLP

2019

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Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell | Ananya Ganesh | Andrew McCallum
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.

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The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Sheshera Mysore | Zachary Jensen | Edward Kim | Kevin Huang | Haw-Shiuan Chang | Emma Strubell | Jeffrey Flanigan | Andrew McCallum | Elsa Olivetti
Proceedings of the 13th Linguistic Annotation Workshop

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.

2018

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Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?
Emma Strubell | Andrew McCallum
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell and McCallum, 2018), a strong, linguistically-informed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo out-performs typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still out-perform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP.

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Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
Patrick Verga | Emma Strubell | Andrew McCallum
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. All-pairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives.

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Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell | Patrick Verga | Daniel Andor | David Weiss | Andrew McCallum
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.

2017

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Dependency Parsing with Dilated Iterated Graph CNNs
Emma Strubell | Andrew McCallum
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs’ capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.

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Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Emma Strubell | Patrick Verga | David Belanger | Andrew McCallum
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds.

2016

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Multilingual Relation Extraction using Compositional Universal Schema
Patrick Verga | David Belanger | Emma Strubell | Benjamin Roth | Andrew McCallum
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Learning Dynamic Feature Selection for Fast Sequential Prediction
Emma Strubell | Luke Vilnis | Kate Silverstein | Andrew McCallum
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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