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Register a new userGrammatical Error Generation Based on Translated Fragments
توليد الأخطاء النحوية بناء على شظايا مترجمة
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نحن نؤدي الترجمة الآلة العصبية لشظايا الجملة من أجل إنشاء كميات كبيرة من البيانات التدريبية لتصحيح الخطأ النحوي الإنجليزي.تهدف أسلوبنا إلى محاكاة الأخطاء التي يرتكبها المتعلمين باللغة الثانية، وتنتج مجموعة واسعة من لغة الأسلوب غير الأصلية مقارنة بنموذج خط الأساس للحديث.نحن نفذ التقييم الكمي والنوعي.يتم عرض طريقتنا لتفوق خط الأساس على البيانات ذات نسبة عالية من الأخطاء.
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulating mistakes made by second language learners, and produces a wider range of non-native style language in comparison to a state-of-the-art baseline model. We carry out quantitative and qualitative evaluation. Our method is shown to outperform the baseline on data with a high proportion of errors.
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In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT. ERRANT functions as a rule-based error type classifier and was used as the main evaluation tool of the systems parti
cipating in the BEA-2019 (Bryant et al., 2019) shared task. Here, we discuss grammatical and morphological differences between English and Greek and how these differences affected the development of ELERRANT. We also introduce the first Greek Native Corpus (GNC) and the Greek WikiEdits Corpus (GWE), two new evaluation datasets with errors from native Greek learners and Wikipedia Talk Pages edits respectively. These two datasets are used for the evaluation of ELERRANT. This paper is a sole fragment of a bigger picture which illustrates the attempt to solve the problem of low-resource languages in NLP, in our case Greek.
Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficie
ncy can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.
This paper offers a comparative evaluation of four commercial ASR systems which are evaluated according to the post-editing effort required to reach publishable'' quality and according to the number of errors they produce. For the error annotation ta
sk, an original error typology for transcription errors is proposed. This study also seeks to examine whether there is a difference in the performance of these systems between native and non-native English speakers. The experimental results suggest that among the four systems, Trint obtains the best scores. It is also observed that most systems perform noticeably better with native speakers and that all systems are most prone to fluency errors.
In this paper, we present a new method for training a writing improvement model adapted to the writer's first language (L1) that goes beyond grammatical error correction (GEC). Without using annotated training data, we rely solely on pre-trained lang
uage models fine-tuned with parallel corpora of reference translation aligned with machine translation. We evaluate our model with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English. We show that our model is able to address specific L1-influenced writing and more complex linguistic phenomena than existing methods, outperforming what a state-of-the-art GEC system can achieve in this regard. Our code and data are open to other researchers.
Grammatical rules are deduced from Arabic spoken by ideally intuitive Arabic
speakers, and illustration is the spirit of the rule, endowing it with life, pleasure, and
originality. The Arabic used in illustration is that of the holy Quran, sayings
of the Prophet
as well as renowned poetic and prosaic statements by Arabs from the Jahileah period up to
150 Hizra,i.e, the end of the period of providing arguments. The term illustration is an
original Arabic term that came out of Arab concern over mistakes in Arabic. The holy
Quran is the source of illustrations, as it is the pillar upon which all other illustrations
depend.
This paper tries to study the relationship between the grammatical rule and
illustrations as well as to demonstrate the motives for illustration, its mechanism,
principles, and sources. It also tries to address some equivalents such as provision of
argument and evidence as well as analogy.
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