Papers by Ana Marasović
Effective Attention Sheds Light On Interpretability (2021.findings-acl)
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| Challenge: | Using a subset of the GLUE tasks and BERT, we compare the two attention matrices and show that their interpretations differ. |
| Approach: | They propose to use visualizing effective attention to interpret a transformer's behavior since it is more pertinent to the model output by design. |
| Outcome: | The proposed method is more relevant to the model output by design than visualizing attention weights. |
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021.emnlp-main)
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Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt Gardner
| Challenge: | Large text corpora are often introduced with minimal documentation . documenting collection process, composition, intended uses, and other are key for structured, task-specific datasets. |
| Approach: | They propose to document a dataset created by applying filters to a single snapshot of Common Crawl. |
| Outcome: | The proposed dataset shows that blocklist filtering removes text from minority individuals and patents. |
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs (2020.findings-emnlp)
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| Challenge: | Existing models that use natural language rationales provide intuitive, higher-level explanations that are easily understandable by humans. |
| Approach: | They propose a model that generates free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. |
| Outcome: | The proposed model generates free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. |
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ (2020.emnlp-demos)
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Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, Zhen Nie
| Challenge: | Efficient data collection is important for advancing research and building time-sensitive applications. |
| Approach: | They propose an open-source platform that standardizes the data collection pipeline . it includes customizable user interface components, automated annotator qualification, and saved pipelines . |
| Outcome: | The proposed platform simplifies data annotation significantly on diverse datasets . it can be used by researchers and engineers to improve reproducibility and minimize overhead . |
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (D19-1)
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| Challenge: | Existing reading comprehension benchmarks do not contain complex coreferential phenomena . obtaining questions focused on such phenomena is difficult because of lexical cues . |
| Approach: | They propose to use a crowdsourced dataset to examine the ability of models to resolve coreference among entities in Wikipedia paragraphs. |
| Outcome: | The proposed model performs significantly worse than humans on the reading comprehension benchmark . paragraphs and other longer texts typically make multiple references to the same entities . |
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020.acl-main)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
| Approach: | They propose to tailor a pretrained model to the domain of a target task by using domain-adaptive pretraining in-domain. |
| Outcome: | The proposed model can be tailored to the domain of a target task and perform well under both high- and low-resource settings. |
Promoting Graph Awareness in Linearized Graph-to-Text Generation (2021.findings-acl)
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| Challenge: | Recent applications of pretrained transformers to linearizations of graph inputs yield stateof-the-art results on graph-to-text tasks. |
| Approach: | They propose to use pretrained transformers to encode local graph structures . they find they can improve the quality of models' implicit graph encodings . |
| Outcome: | The proposed models can encode local graph structures and reconstruct corrupted inputs. |
Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
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| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
Measuring Association Between Labels and Free-Text Rationales (2021.emnlp-main)
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| Challenge: | Existing models for extractive rationales do not work as well on reasoning tasks requiring free-text rationale. |
| Approach: | They propose to use pipelines to extract rationales from input words and to use them to explain reasoning tasks. |
| Outcome: | The proposed models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. |
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling (N18-1)
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| Challenge: | Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). |
| Approach: | They propose to use multi-task learning to improve Opinion Role Labeling by using a related task which has substantially more data. |
| Outcome: | The proposed model outperforms the state-of-the-art model for Opinion Role Labeling (ORL) with more data. |