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Found 29 papers, 6 papers with code
Emergent Word Order Universals from Cognitively-Motivated Language Models
This also showcases the advantage of cognitively-motivated LMs, which are typically employed in cognitive modeling, in the computational simulation of language universals.
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text.
Analyzing Neural Discourse Coherence Models
In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation.
The BEA-2019 Shared Task on Grammatical Error Correction
This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
Text Readability Assessment for Second Language Learners
This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners.
Automatic learner summary assessment for reading comprehension
Automating the assessment of learner summaries provides a useful tool for assessing learner reading comprehension.
Language Model Based Grammatical Error Correction without Annotated Training Data
Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated.
The Effect of Adding Authorship Knowledge in Automated Text Scoring
Some language exams have multiple writing tasks.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences.
Curriculum Q-Learning for Visual Vocabulary Acquisition
The structure of curriculum plays a vital role in our learning process, both as children and adults.
An Error-Oriented Approach to Word Embedding Pre-Training
Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.
Artificial Error Generation with Machine Translation and Syntactic Patterns
Shortage of available training data is holding back progress in the area of automated error detection.
Ranked #3 on
Grammatical Error Detection
on FCE
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated.
Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies.
