Pith. sign in

REVIEW 1 cited by

Relation Classification as Two-way Span-Prediction

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 2010.04829 v2 pith:KYNDVHQZ submitted 2020-10-09 cs.CL

Amir DN Cohen , Shachar Rosenman , Yoav Goldberg This is my paper · ORCID
classification cs.CL
keywords classificationrelationspan-predictiontaskbetterembeddingobjectivesupervised
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as span-prediction (SP) problem, similar to Question answering (QA). We present a span-prediction based system for RC and evaluate its performance compared to the embedding based system. We demonstrate that the supervised SP objective works significantly better then the standard classification based objective. We achieve state-of-the-art results on the TACRED and SemEval task 8 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. Diversity Over Quantity: A Lesson From Few Shot Relation Classification

    cs.CL 2024-12 conditional novelty 5.0 of 10

    Holding training set size fixed, increasing the number of relation types improves few-shot relation classification performance on unseen relations, demonstrated in the new REBEL-FS benchmark.

Pith tools