Pith. sign in

REVIEW 1 cited by

Multi-Sentence Argument Linking

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1911.03766 v3 pith:K6O5LCXV submitted 2019-11-09 cs.CL

Seth Ebner , Patrick Xia , Ryan Culkin , Kyle Rawlins , Benjamin Van Durme This is my paper · ORCID
classification cs.CL
keywords modelacrossargumentdatasetslinkingramsrolesannotated
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MPL: Multiple Programming Languages with Large Language Models for Information Extraction

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Using multiple programming languages as code-style prompts during fine-tuning improves LLM information extraction accuracy over single-language prompting.

Pith tools