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Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays

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arxiv 1606.03144 v1 pith:ZCQOGHSE submitted 2016-06-09 cs.CL cs.LGcs.NE

Marek Rei , Ronan Cummins This is my paper · ORCID
classification cs.CLcs.LGcs.NE
keywords learnerembeddingsessaysneuralrelevancesentence-levelsimilaritytask
verification ladder T0 review T1 audit T2 compute T3 formal

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We investigate the task of assessing sentence-level prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentence-level similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.

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Cited by 2 Pith papers

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  1. UKTA: Unified Korean Text Analyzer

    cs.CL 2025-02 conditional novelty 5.0 of 10

    Adding 294 essay-level lexical features to a KoBERT+BiGRU model raises Korean essay scoring accuracy from 0.649 to 0.657 and quadratic weighted kappa from 0.509 to 0.538 on the AI-HUB Essay Evaluation Dataset.

  2. Word Embedding for Response-To-Text Assessment of Evidence

    cs.CL 2019-08 conditional novelty 4.0 of 10

    Using word embeddings to relax exact word matching in rubric features modestly improves automated evidence scoring for elementary response-to-text essays, with task-trained skip-gram best on lower-grade and cross-age tests.

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