Search Results for author:
Found 21 papers, 7 papers with code
NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge
News article revision histories provide clues to narrative and factual evolution in news articles.
Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification
As labeling schemas evolve over time, small differences can render datasets following older schemas unusable.
Ranked #1 on
Text Classification
on NewsDiscourse
NewsEdits 2.0: Learning the Intentions Behind Updating News
As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts.
NewsHomepages: Homepage Layouts Capture Information Prioritization Decisions
To illustrate that modeling organizational hierarchies has broader implications, we applied our models to rank-order a collection of local city council policies passed over a ten-year period in San Francisco, assessing their "newsworthiness".
NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews
Large Language Models (LLMs) have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue.
PatentEdits: Framing Patent Novelty as Textual Entailment
A patent must be deemed novel and non-obvious in order to be granted by the US Patent Office (USPTO).
Explaining Mixtures of Sources in News Articles
We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation.
Are Large Language Models Capable of Generating Human-Level Narratives?
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression.
Tracking the Newsworthiness of Public Documents
Journalists must find stories in huge amounts of textual data (e. g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us understand coverage patterns and help us build assistive tools.
Stay on topic with Classifier-Free Guidance
Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations.
Ranked #1 on
Text Generation
on SciQ
Identifying Informational Sources in News Articles
News articles are driven by the informational sources journalists use in reporting.
Sequentially Controlled Text Generation
While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure.
If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles
Developing and improving computational approaches to covering news can increase journalistic output and improve the way stories are covered.
NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge
News article revision histories provide clues to narrative and factual evolution in news articles.
StateCensusLaws.org: A Web Application for Consuming and Annotating Legal Discourse Learning
In this work, we create a web application to highlight the output of NLP models trained to parse and label discourse segments in law text.
Modeling "Newsworthiness" for Lead-Generation Across Corpora
Journalists obtain "leads", or story ideas, by reading large corpora of government records: court cases, proposed bills, etc.
NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)
In this work, we present, to our knowledge, the first publicly available dataset of news article revision histories, or NewsEdits.
"Don't quote me on that": Finding Mixtures of Sources in News Articles
Journalists publish statements provided by people, or \textit{sources} to contextualize current events, help voters make informed decisions, and hold powerful individuals accountable.
Multitask Learning for Class-Imbalanced Discourse Classification
Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures.
Actionable Recourse in Linear Classification
We present integer programming tools to ensure recourse in linear classification problems without interfering in model development.
Bayesian Nonparametrics in Topic Modeling: A Brief Tutorial
Using nonparametric methods has been increasingly explored in Bayesian hierarchical modeling as a way to increase model flexibility.
