9 citations · 12 across the 3 of their papers we have counts for
6 papers
Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment
Lily Ng, Anne Lauscher, Joel Tetreault +1
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…
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
Anne Lauscher, Lily Ng, Courtney Napoles +1
Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual…
How do you correct run-on sentences it's not as easy as it seems
Junchao Zheng, Courtney Napoles, Joel Tetreault +1
Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences…
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Courtney Napoles, Keisuke Sakaguchi, Joel Tetreault
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a br…
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Courtney Napoles, Keisuke Sakaguchi, Joel Tetreault
Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing grammatical…
GLEU Without Tuning
Courtney Napoles, Keisuke Sakaguchi, Matt Post +1
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 annotat…