English Grammar Error Correction

[Violette Ransone]

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GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:

The CoNLL-2014 shared task test set is the most widely used dataset to benchmark GEC systems. The test set contains 1,312 English sentences with error annotations by 2 expert annotators. Models are evaluated with MaxMatch scorer (Dahlmeier and Ng, 2012) which computes a span-based Fβ-score (β set to 0.5 to weight precision twice as recall).

The shared task setting restricts that systems use only publicly available datasets for training to ensure a fair comparison between systems. The highest published scores on the the CoNLL-2014 test set are given below. A distinction is made between papers that report results in the restricted CoNLL-2014 shared task setting of training using publicly-available training datasets only (Restricted) and those that made use of large, non-public datasets (Unrestricted).

BEA shared task - 2019 dataset released for the BEA Shared Task on Grammatical Error Correction provides a newer and bigger dataset for evaluating GEC models in 3 tracks, based on the datasets used for training:

Since current state-of-the-art systems rely on as much annotated learner data as possible to reach the best performance, the goal of the low resource track is to encourage research into systems that do not rely on large amounts of learner data. This track should be of particular interest to researchers working on GEC for languages where large learner corpora do not exist.

In almost any paper there is an abundance of classical errors, such as omitting the "s" at the end of a third person singular verb. I am growing tired of painstakingly listing the page and line for each error. Is it acceptable to just write: "Please, have a native speaker fix your grammar?"

I usually list a few examples and suggest editing by a native speaker/writer. If it's really bad, I will do so as a request for major revision. I've never had an editor complain about my doing it that way.

Usually, grammatical errors don't actually effect your ability to evaluate the science in a paper. Even when phrases are fairly tangled or when a missing word makes a sentence say the opposite of what is intended, you can usually sort out what the authors intended, and judge them on their science, not their presentation.

Whatever grammatical issues you point out, be explicit that they are not the reason for your recommendation. Some reviewers will play language police, and recommend a paper be rejected because it is "sloppy." This is, in my opinion, inexcusable: grammar, no matter how tangled, can always be cleaned up, and should only be held against an author if they refuse to do such cleanup.

In those rare cases that things are so badly presented that you cannot understand the science, however, state clearly that is what has happened, and that this is why you are judging the grammar to actually affect the acceptability of the paper.

It depends on how bad the problem is. If the number of grammatical errors is reasonably small, I'd be inclined to point them out individually, but if there are errors all over the place, I'd point out a few (for example, those on the first page, or in the first paragraph if there are too many on the first page), say that there are many more, and recommend that the paper be repaired by a native speaker. (This assumes that the author is not a native speaker; if (s)he is, then I'd recommend careful proofreading. I have refereed papers that had obviously not been proofread even in the most cursory manner.)

I would stay away from the comment about having a "native speaker fix your grammar" since even though it is a valid comment its not exactly constructive or guiding the author back to the correct path.

Depending on the general level of errors, I typically would mention one or 2 instances of a given error specifically as a single item listing its locations in the paper, and then in the event that it appears a third time then change the review comment to a major item and change the text of the comment to reflect that these are limited examples and the paper contains more identical instances of the same issue.

You don't need to list every mistake. As people have said, you can just give a few examples and ask the authors to look back over the whole paper. I also wouldn't say "Please, have a native speaker fix your grammar". You can make the same point that there are grammatical problems which need to be fixed without making assumptions about the authors which may themselves be offensive.

Where I would slightly disagree with some of the other answers is that I do think all non-trivial grammatical errors should be fixed before publication. This includes getting singulars and plurals right for example. An arguably incorrect semi-colon may be more forgiveable of course. I have read a number of papers with poor English where it has made it much harder to understand the content of the paper.

I think grammar and spelling mistakes can impair readability of a paper quite a lot, and it is good the get rid of as many of them as possible before publication. Instead of listing every mistake, though (along with page and line number - a tedious undertaking) I made it a habit to mark them directly on the article PDF (using the "highlighter" function, this is fast and easy, and can be done while reading through the paper). When handing in the review I attach the PDF along with a note, telling the authors to look into the errors marked in the file.

Recently one of my former students contacted me on this topic. She is currently studying to be a language teacher and taking university courses. She has attended the Agen Workshop for the last couple of years and has heard Dr. Krashen speak more than once. She was surprised to hear one of her professors, who seems to respect our eminent friend, support error correction. She asked me for articles and references on the topic, and of course I wrote to dear Stephen. This is his reply:

Every summer for the last ten years something magic has happened in Agen, France. Teachers from around the world have gathered in a friendly little town in southwest France and particpated in what many of them have called a life-changing experience. They come together because they have heard of a different way of teaching languages, a way of creating stories with their students and building a different kind of classroom. They come with open hearts and open minds and they leave with smiles and warm memories and many new friends. That is the magic of Agen.

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CoNLL-2014 will continue the CoNLL tradition of having a high profile shared task in natural language processing. This year's shared task will be grammatical error correction, a continuation of the CoNLL shared task in 2013. A participating system in this shared task is given short English texts written by non-native speakers of English. The system detects the grammatical errors present in the input texts, and returns the corrected essays. The shared task in 2014 will require a participating system to correct all errors present in an essay (i.e., not restricted to just five error types in 2013). Also, the evaluation metric will be changed to F0.5, weighting precision twice as much as recall.

The grammatical error correction task is impactful since it is estimated that hundreds of millions of people in the world are learning English and they benefit directly from an automated grammar checker. However, for many error types, current grammatical error correction methods do not achieve a high performance and thus more research is needed.

Grammatical error correction (GEC) attempts to model grammar and other types of writing errors in order to provide grammar and spelling suggestions, improving the quality of written output in documents, emails, blog posts and even informal chats. Over the past 15 years, there has been a substantial improvement in GEC quality, which can in large part be credited to recasting the problem as a "translation" task. When introduced in Google Docs, for example, this approach resulted in a significant increase in the number of accepted grammar correction suggestions.

One of the biggest challenges for GEC models, however, is data sparsity. Unlike other natural language processing (NLP) tasks, such as speech recognition and machine translation, there is very limited training data available for GEC, even for high-resource languages like English. A common remedy for this is to generate synthetic data using a range of techniques, from heuristic-based random word- or character-level corruptions to model-based approaches. However, such methods tend to be simplistic and do not reflect the true distribution of error types from actual users.

The tagged corruption model that we propose builds on this idea by taking a clean sentence as input along with an error type tag that describes the kind of error one wishes to reproduce. It then generates an ungrammatical version of the input sentence that contains the given error type. Choosing different error types for different sentences increases the diversity of corruptions compared to a conventional corruption model.

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