Pith. sign in

REVIEW 1 cited by

Neural Multi-task Learning in Automated Assessment

Not yet reviewed by Pith; the record is open.

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 1801.06830 v1 pith:L5IHLK2A submitted 2018-01-21 cs.CL

classification cs.CL
keywords automatedessaydetectionerrorneuraltasksassessmentgrammatical
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.

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. Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Using grammar-corrected essays as a second input improves cross-prompt trait scoring, with the largest gains on grammar-related traits like Conventions.

Pith tools