Papers by Daniel Weld
S2ORC: The Semantic Scholar Open Research Corpus (2020.acl-main)
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| Challenge: | Academic papers are an increasingly important textual domain for natural language processing (NLP) research. |
| Approach: | They propose to aggregate 81.1M English-language academic papers into a unified source . they hope this resource will facilitate research and development of tools for text mining over academic text. |
| Outcome: | The proposed corpus includes metadata, abstracts, bibliographic references, and structured full text for 8.1M open access papers. |
PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents (2023.emnlp-demo)
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Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel Weld, Doug Downey, Luca Soldaini
| Challenge: | Existing tools for working with scientific documents are limited and documents are often in difficult-to-use PDF formats. |
| Approach: | They propose an open-source Python toolkit for analyzing and processing visually-rich scientific documents. |
| Outcome: | PaperMage provides turn-key recipes for common scientific document processing use-cases. |
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models (2021.acl-long)
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| Challenge: | Existing counterfactual generation methods rely on manual labor to create very few counterf actuals or only instantiate limited types of perturbations such as paraphrases or word substitutions. |
| Approach: | They propose a general-purpose counterfactual generator that allows for control over perturbation types and locations. |
| Outcome: | The proposed generator produces diverse sets of realistic counterfactuals that are useful in various applications. |
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search (2020.emnlp-demos)
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Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel Weld, Marti Hearst, Jevin West
| Challenge: | SciSight is a system for exploratory search of COVID-19 literature . it explores associations between biomedical facets extracted from papers . |
| Approach: | They propose a system for exploratory search of COVID-19 literature that integrates two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers; second, combining textual and network information to search and visualize groups of researchers and their ties. |
| Outcome: | The proposed system has served over 15K users with over 42K page views and 13% returns. |
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference (N19-1)
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| Challenge: | Existing inference models that rely heavily on unsupervised single-word embeddings struggle to learn implied relationships between pairs of words. |
| Approach: | They propose to use word embeddings to learn and use background knowledge about implied relationships between words that are crucial for cross-sentence inference problems. |
| Outcome: | The proposed models gain 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI, and 8.8% on the adversarial SQu AD datasets. |
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (2022.emnlp-demos)
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Sonia Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jonathan Borchardt, Daniel Weld, Tom Hope, Doug Downey
| Challenge: | Current systems that automatically define unfamiliar terms only surface a single "best" description for all users, which may not be accessible for all readers, given varying background knowledge. |
| Approach: | They propose an end-to-end system that generates sets of descriptions of scientific concepts . ACCoRD corpus includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions . |
| Outcome: | The proposed system produces diverse descriptions of concepts in terms of reference concepts. |
Errudite: Scalable, Reproducible, and Testable Error Analysis (P19-1)
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| Challenge: | Errudite is an interactive tool for informative error analysis. |
| Approach: | They propose an interactive tool for model and task agnostic error analysis . they propose a model-agnosed approach to model and tasks error analysis with Errudite . |
| Outcome: | The proposed tool enables reproducible error analyses with less effort and ambiguities in previous tools. |
TLDR: Extreme Summarization of Scientific Documents (2020.findings-emnlp)
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| Challenge: | TLDR generation requires expert background knowledge and understanding of complex domain-specific language. |
| Approach: | They propose a learning strategy that exploits titles as an auxiliary training signal. |
| Outcome: | The proposed method improves upon strong baselines under both automated metrics and human evaluations. |
SPECTER: Document-level Representation Learning using Citation-informed Transformers (2020.acl-main)
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| Challenge: | Recent Transformer language models do not leverage information on inter-document relatedness, which limits their document-level representation power. |
| Approach: | They propose a method to generate document-level embeddings using citation graphs. |
| Outcome: | The proposed method outperforms baselines on document-level tasks. |
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation (2022.emnlp-main)
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Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel Weld
| Challenge: | Effective human evaluation of text generation tasks remains an important, open area for research. |
| Approach: | They propose a system for running standardized human evaluations across different generation tasks. |
| Outcome: | The proposed system produces standardized human evaluations across tasks . it crowdsources predictions and ranks systems on leaderboards . the proposed system is not reproducible over time and different annotator populations . |
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)
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Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi
| Challenge: | COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge . |
| Approach: | They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available . |
| Outcome: | The proposed schema outperforms PubMed search in clinical trials. |
BERT for Coreference Resolution: Baselines and Analysis (D19-1)
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| Challenge: | Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition. |
| Approach: | They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks. |
| Outcome: | A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing. |
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models (2024.emnlp-main)
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Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel Weld, Joseph Chee Chang, Kyle Lo
| Challenge: | Using language models (LMs) can generate literature review tables by decomposing it into separate schema and value generation steps. |
| Approach: | They propose a framework that leverages language models to perform literature review table generation by decomposing it into separate schema and value generation steps. |
| Outcome: | The proposed framework decomposes the task into two sub-tasks: schema generation and value generation. |
Semi-Supervised Event Extraction with Paraphrase Clusters (N18-2)
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| Challenge: | Existing event extraction systems are limited in their accuracy due to the lack of available training data. |
| Approach: | They propose a method for self-training event extraction systems by bootstrapping additional training data. |
| Outcome: | The proposed method improves on ACE 2005 and TAC-KBP 2015 datasets. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |