Mariano Felice


2020

pdf bib
A Crash Course in Automatic Grammatical Error Correction
Roman Grundkiewicz | Christopher Bryant | Mariano Felice
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. Although most research has focused on correcting errors in the context of English as a Second Language (ESL), GEC can also be applied to other languages and native text. The main application of a GEC system is thus to assist humans with their writing. Academic and commercial interest in GEC has grown significantly since the Helping Our Own (HOO) and Conference on Natural Language Learning (CoNLL) shared tasks in 2011-14, and a record-breaking 24 teams took part in the recent Building Educational Applications (BEA) shared task. Given this interest, and the recent shift towards neural approaches, we believe the time is right to offer a tutorial on GEC for researchers who may be new to the field or who are interested in the current state of the art and future challenges. With this in mind, the main goal of this tutorial is not only to bring attendees up to speed with GEC in general, but also examine the development of neural-based GEC systems.

2019

pdf bib
The BEA-2019 Shared Task on Grammatical Error Correction
Christopher Bryant | Mariano Felice | Øistein E. Andersen | Ted Briscoe
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write&Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F_0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.

pdf bib
Entropy as a Proxy for Gap Complexity in Open Cloze Tests
Mariano Felice | Paula Buttery
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

This paper presents a pilot study of entropy as a measure of gap complexity in open cloze tests aimed at learners of English. Entropy is used to quantify the information content in each gap, which can be used to estimate complexity. Our study shows that average gap entropy correlates positively with proficiency levels while individual gap entropy can capture contextual complexity. To the best of our knowledge, this is the first unsupervised information-theoretical approach to evaluating the quality of cloze tests.

2017

pdf bib
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Christopher Bryant | Mariano Felice | Ted Briscoe
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.

pdf bib
Artificial Error Generation with Machine Translation and Syntactic Patterns
Marek Rei | Mariano Felice | Zheng Yuan | Ted Briscoe
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.

2016

pdf bib
Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
Mariano Felice | Christopher Bryant | Ted Briscoe
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.

pdf bib
Candidate re-ranking for SMT-based grammatical error correction
Zheng Yuan | Ted Briscoe | Mariano Felice
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

2015

pdf bib
Towards a standard evaluation method for grammatical error detection and correction
Mariano Felice | Ted Briscoe
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

pdf bib
Generating artificial errors for grammatical error correction
Mariano Felice | Zheng Yuan
Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Grammatical error correction using hybrid systems and type filtering
Mariano Felice | Zheng Yuan | Øistein E. Andersen | Helen Yannakoudakis | Ekaterina Kochmar
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

2013

pdf bib
Constrained Grammatical Error Correction using Statistical Machine Translation
Zheng Yuan | Mariano Felice
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

2012

pdf bib
Linguistic Features for Quality Estimation
Mariano Felice | Lucia Specia
Proceedings of the Seventh Workshop on Statistical Machine Translation