Search Results for author: Daniel S. Weld

Found 44 papers, 21 papers with code

In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making

no code implementations12 May 2023 Raymond Fok, Daniel S. Weld

To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision making performance.

Decision Making

An Interactive UI to Support Sensemaking over Collections of Parallel Texts

no code implementations11 Mar 2023 Joyce Zhou, Elena Glassman, Daniel S. Weld

Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors.

ScatterShot: Interactive In-context Example Curation for Text Transformation

1 code implementation14 Feb 2023 Tongshuang Wu, Hua Shen, Daniel S. Weld, Jeffrey Heer, Marco Tulio Ribeiro

ScatterShot iteratively slices unlabeled data into task-specific patterns, samples informative inputs from underexplored or not-yet-saturated slices in an active learning manner, and helps users label more efficiently with the help of an LLM and the current example set.

Active Learning

FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs

no code implementations16 Aug 2022 Harmanpreet Kaur, Doug Downey, Amanpreet Singh, Evie Yu-Yen Cheng, Daniel S. Weld, Jonathan Bragg

We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG.

Knowledge Graphs

A Computational Inflection for Scientific Discovery

no code implementations4 May 2022 Tom Hope, Doug Downey, Oren Etzioni, Daniel S. Weld, Eric Horvitz

We stand at the foot of a significant inflection in the trajectory of scientific discovery.


Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare

no code implementations27 Apr 2022 Marissa Radensky, Dustin Burson, Rajya Bhaiya, Daniel S. Weld

An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions.

Decision Making

From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

no code implementations21 Apr 2022 Hyeonsu B. Kang, Rafal Kocielnik, Andrew Head, Jiangjiang Yang, Matt Latzke, Aniket Kittur, Daniel S. Weld, Doug Downey, Jonathan Bragg

To improve the discovery experience we introduce multiple new methods for \em augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history.

Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search

no code implementations16 Mar 2022 Daniel King, Zejiang Shen, Nishant Subramani, Daniel S. Weld, Iz Beltagy, Doug Downey

Based on our findings, we present PINOCCHIO, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations.

Abstractive Text Summarization

Exploring The Role of Local and Global Explanations in Recommender Systems

no code implementations27 Sep 2021 Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popović, Daniel S. Weld

However, we note that the two explanation approaches may be better compared in the context of a higher-stakes or more opaque domain.

Recommendation Systems

A Search Engine for Discovery of Scientific Challenges and Directions

1 code implementation NeurIPS Workshop AI4Scien 2021 Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope

To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.

VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups

1 code implementation1 Jun 2021 Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug Downey

Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically-labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines.

Language Modelling Text Classification +2

Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models

1 code implementation ACL 2021 Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, Daniel S. Weld

While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions.

Text Generation

Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions

1 code implementation EMNLP (sdp) 2020 Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst

Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

1 code implementation29 Sep 2020 Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst

We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols.

High-Precision Extraction of Emerging Concepts from Scientific Literature

1 code implementation11 Jun 2020 Daniel King, Doug Downey, Daniel S. Weld

From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15, 000 extractions.

Vocal Bursts Intensity Prediction

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

no code implementations EMNLP 2020 Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel S. Weld, Marti A. Hearst, Jevin West

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.

Language Modelling

The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America

2 code implementations4 May 2020 Benjamin Charles Germain Lee, Jaime Mears, Eileen Jakeway, Meghan Ferriter, Chris Adams, Nathan Yarasavage, Deborah Thomas, Kate Zwaard, Daniel S. Weld

We report the results of running the pipeline on 16. 3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced.

Optical Character Recognition (OCR)

Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork

no code implementations27 Apr 2020 Gagan Bansal, Besmira Nushi, Ece Kamar, Eric Horvitz, Daniel S. Weld

To optimize the team performance for this setting we maximize the team's expected utility, expressed in terms of the quality of the final decision, cost of verifying, and individual accuracies of people and machines.

Decision Making

SPECTER: Document-level Representation Learning using Citation-informed Transformers

5 code implementations ACL 2020 Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld

We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.

Citation Prediction Document Classification +3

LIMEADE: From AI Explanations to Advice Taking

1 code implementation9 Mar 2020 Benjamin Charles Germain Lee, Doug Downey, Kyle Lo, Daniel S. Weld

We show our method improves accuracy compared to a rigorous baseline on the image classification domains.

BIG-bench Machine Learning Image Classification +1

Pretrained Language Models for Sequential Sentence Classification

1 code implementation IJCNLP 2019 Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Daniel S. Weld

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document.

Classification General Classification +1

BERT for Coreference Resolution: Baselines and Analysis

2 code implementations IJCNLP 2019 Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3. 9 F1) and GAP (+11. 5 F1) benchmarks.

Ranked #10 on Coreference Resolution on CoNLL 2012 (using extra training data)

pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

3 code implementations NAACL 2019 Mandar Joshi, Eunsol Choi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer

Reasoning about implied relationships (e. g., paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems.

Common Sense Reasoning Word Embeddings

Semi-Supervised Event Extraction with Paraphrase Clusters

no code implementations NAACL 2018 James Ferguson, Colin Lockard, Daniel S. Weld, Hannaneh Hajishirzi

Supervised event extraction systems are limited in their accuracy due to the lack of available training data.

Event Extraction

StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow

1 code implementation26 Mar 2018 Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, Huan Sun

In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them).


The Challenge of Crafting Intelligible Intelligence

no code implementations9 Mar 2018 Daniel S. Weld, Gagan Bansal

Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand.

Stochastic Optimization

A Programming Language With a POMDP Inside

no code implementations31 Aug 2016 Christopher H. Lin, Mausam, Daniel S. Weld

We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert practitioners.

Extreme Extraction: Only One Hour per Relation

no code implementations21 Jun 2015 Raphael Hoffmann, Luke Zettlemoyer, Daniel S. Weld

Information Extraction (IE) aims to automatically generate a large knowledge base from natural language text, but progress remains slow.

Design Challenges for Entity Linking

no code implementations TACL 2015 Xiao Ling, Sameer Singh, Daniel S. Weld

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference.

Entity Linking Relation Extraction +1

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