GLEN: General-Purpose Event Detection for Thousands of Types

The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 205K event mentions with 3,465 different types, making it more than 20x larger in ontology than today's largest event dataset. GLEN is created by utilizing the DWD Overlay, which provides a mapping between Wikidata Qnodes and PropBank rolesets. This enables us to use the abundant existing annotation for PropBank as distant supervision. In addition, we also propose a new multi-stage event detection model CEDAR specifically designed to handle the large ontology size in GLEN. We show that our model exhibits superior performance compared to a range of baselines including InstructGPT. Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset. Our dataset, code, and models are released at \url{https://github.com/ZQS1943/GLEN}.}Comment: Accepted to EMNLP 2023. The first two authors contributed equally. (16 pages

Rab4 Orchestrates a Small GTPase Cascade for Recruitment of Adaptor Proteins to Early Endosomes

SummaryBackgroundEarly, sorting endosomes are a major crossroad of membrane traffic, at the intersection of the endocytic and exocytic pathways. The sorting of endosomal cargo for delivery to different subcellular destinations is mediated by a number of distinct coat protein complexes, including adaptor protein 1 (AP-1), AP-3, and Golgi-localized, gamma adaptin ear-containing, Arf-binding (GGAs) protein. Ultrastructural studies suggest that these coats assemble onto tubular subdomains of the endosomal membrane, but the mechanisms of coat recruitment and assembly at this site remain poorly understood.ResultsHere we report that the endosomal Rab protein Rab4 orchestrates a GTPase cascade that results in the sequential recruitment of the ADP-ribosylation factor (Arf)-like protein Arl1; the Arf-specific guanine nucleotide exchange factors BIG1 and BIG2; and the class I Arfs, Arf1 and Arf3. Knockdown of Arf1, or inhibition of BIG1 and BIG2 activity with brefeldin A results in the loss of AP-1, AP-3, and GGA-3, but not Arl1, from endosomal membranes and the formation of elongated tubules. In contrast, depletion of Arl1 randomizes the distribution of Rab4 on endosomal membranes, inhibits the formation of tubular subdomains, and blocks recruitment of BIG1 and BIG2, Arfs, and adaptor protein complexes to the endosome.ConclusionsTogether these findings indicate that Arl1 links Rab4-dependent formation of endosomal sorting domains with downstream assembly of adaptor protein complexes that constitute the endosomal sorting machinery

NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge

Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating news misinformation. However, most existing work has focused on claim sentence analysis while overlooking crucial background attributes (e.g., claimer, claim objects). In this work, we present NewsClaims, a new benchmark for knowledge-aware claim detection in the news domain. We redefine the claim detection problem to include extraction of additional background attributes related to each claim and release 889 claims annotated over 143 news articles. NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. To this end, we provide a comprehensive evaluation of zero-shot and prompt-based baselines for NewsClaims.Comment: Preprin

Idioms: Easily Identified or Needles in a Haystack?

HDLS15 Presentatio