In this paper, we present a new method based on machine translation for correcting serial grammatical errors in a given sentence in learners’ writing. In our approach, translation models are generated to translate the input into a grammatical sentence. The method involves automatically learning two translation models that are based on Web-scale n-grams. The first model translates trigrams conta...

In this paper we discuss our approach toward establishing a model of the acquisition of English grammatical structures by users of our English language tutoring system, which has been designed for deaf users of American Sign Language. We explore the correlation between a corpus of error-tagged texts and their holistic proficiency scores assigned by experts in order to draw initial conclusions a...

This paper reports on a study thatinvestigated Iranian EFL high school students’ perceptions of written grammar feedback to specify their reasons for preferring comprehensive or selective feedback and choosing some feedback strategies. A questionnaire was administered to 100 EFL intermediate high school students who were selected based on their scores on a proficiency test. Moreover, semi-struc...

Despite the recent advances in the field of machine translation (MT), MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. Detecting and highlighting errors in machine-translated sentences can help post-editors to focus on the erroneous fragments that need to be corrected. This paper presents two methods for detecting grammatical ...

Various word-processing system have been developed to identify grammatical errors and mark learners’ essays. However, they are not specifically developed for Malaysian ESL (English as a second language) learners. A marking tool which is capable to identify errors in ESL writing for these learners is very much needed. Though there are numerous techniques adopted in grammar checking and automated...

The present paper proposes automatic error analysis methods that use patterns representing grammatical check points. Our method is comparable to or slightly outperforms conventional methods for automatic evaluation metrics. Different from the conventional methods, our method enables error analysis for each grammatical check point. While our method does not depend on languages, we experimentally...

This is a report on the methods used and results obtained by the UW-Stanford team for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016 on grammatical error detection. This team developed a symbolic grammar-based system augmented with manually defined mal-rules to accommodate and identify instances of highfrequency grammatical errors. System results were entered both for th...

Even with high overall parsing accuracy, datadriven parsers often make errors in the assignment of core grammatical functions such as subject and object. Starting from a detailed error analysis of a state-of-the-art dependency parser for Swedish, we show that the addition of linguistically motivated features targeting specific error types may lead to substantial improvements, both for specific ...

Theories of language production have long been expressed as connectionist models. We outline the issues and challenges that must be addressed by connectionist models of lexical access and grammatical encoding, and review three recent models. The models illustrate the value of an interactive activation approach to lexical access in production, the need for sequential output in both phonological ...

This paper describes a possible extension for a well-known open source grammar checking software LanguageTool. The proposed extension allows the developers to write grammatical rules that rely on natural language parser-supplied dependency trees. Such rules are indispensable for the analysis of word-word links in order to handle a variety of grammatical errors, including improper use of article...