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Found 19 papers, 9 papers with code
Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment
Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity.
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory.
Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays.
How do you correct run-on sentences it's not as easy as it seems
Run-on sentences are common grammatical mistakes but little research has tackled this problem to date.
Systematically Adapting Machine Translation for Grammatical Error Correction
Our model rivals the current state of the art using a fraction of the training data.
GEC into the future: Where are we going and how do we get there?
The field of grammatical error correction (GEC) has made tremendous bounds in the last ten years, but new questions and obstacles are revealing themselves.
Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus
This work presents a dataset and annotation scheme for the new task of identifying {``}good{''} conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations.
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC).
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading reference-based evaluation metrics.
GLEU Without Tuning
The GLEU metric was proposed for evaluating grammatical error corrections using n-gram overlap with a set of reference sentences, as opposed to precision/recall of specific annotated errors (Napoles et al., 2015).
Optimizing Statistical Machine Translation for Text Simplification
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus.
Ranked #7 on
Text Simplification
on TurkCorpus
Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics.
Problems in Current Text Simplification Research: New Data Can Help
Simple Wikipedia has dominated simplification research in the past 5 years.
