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Register a new userNeural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
تقدير الجودة العصبية مع فرضيات متعددة لتصحيح الخطأ النحوي
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يهدف تصحيح الخطأ النحوي (GEC) إلى تصحيح أخطاء الكتابة ومساعدة المتعلمين في اللغة على تحسين مهاراتهم في الكتابة. ومع ذلك، تميل نماذج GEC الحالية إلى إنتاج تصحيحات زائفة أو تفشل في اكتشاف الكثير من الأخطاء. يعد نموذج تقدير الجودة ضروريا لضمان أن يحصل المتعلمون على نتائج GEC دقيقة وتجنب مضللة من الجمل المصححة بشكل سيء. يمكن أن تولد نماذج GEC المدربة جيدا العديد من الفرضيات عالية الجودة من خلال فك التشفير، مثل البحث الشعاع، والتي توفر أدلة GEC القيمة ويمكن استخدامها لتقييم جودة GEC. ومع ذلك، تهمش النماذج الحالية أدلة GEC المحتملة من فرضيات مختلفة. تقدم هذه الورقة شبكة التحقق العصبية (Vernet) لتقدير جودة GEC مع فرضيات متعددة. تحدد Vernet تفاعلات بين الفرضيات مع رسم بياني للمنطق وإجراء نوعين من آليات الاهتمام لنشر أدلة GEC للتحقق من جودة الفرضيات التي تم إنشاؤها. تظهر تجاربنا على أربع مجموعات بيانات GEC أن Vernet يحصل على أداء اكتشاف الأخطاء النحوية الحديثة، وتحقق أفضل نتائج تقدير الجودة، وتحسين أداء GEC بشكل كبير من خلال فرضيات إعادة النشر. تتوفر جميع رموز البيانات والمصادر في https://github.com/thunlp/vernet.
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available at https://github.com/thunlp/VERNet.
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Grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong
results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical / grammatical pairs for training a corrector. We evaluate our approach on GEC datasets on multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F0.5) and the supervised setting (+0.5 F0.5).
Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Studies on GEC have proposed several methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences. Currently
, a mainstream approach to generate pseudo data is back-translation (BT). Most previous studies using BT have employed the same architecture for both the GEC and BT models. However, GEC models have different correction tendencies depending on the architecture of their models. Thus, in this study, we compare the correction tendencies of GEC models trained on pseudo data generated by three BT models with different architectures, namely, Transformer, CNN, and LSTM. The results confirm that the correction tendencies for each error type are different for every BT model. In addition, we investigate the correction tendencies when using a combination of pseudo data generated by different BT models. As a result, we find that the combination of different BT models improves or interpolates the performance of each error type compared with using a single BT model with different seeds.
This paper presents the NICT Kyoto submission for the WMT'21 Quality Estimation (QE) Critical Error Detection shared task (Task 3). Our approach relies mainly on QE model pretraining for which we used 11 language pairs, three sentence-level and three
word-level translation quality metrics. Starting from an XLM-R checkpoint, we perform continued training by modifying the learning objective, switching from masked language modeling to QE oriented signals, before finetuning and ensembling the models. Results obtained on the test set in terms of correlation coefficient and F-score show that automatic metrics and synthetic data perform well for pretraining, with our submissions ranked first for two out of four language pairs. A deeper look at the impact of each metric on the downstream task indicates higher performance for token oriented metrics, while an ablation study emphasizes the usefulness of conducting both self-supervised and QE pretraining.
In quality estimation (QE), the quality of translation can be predicted by referencing the source sentence and the machine translation (MT) output without access to the reference sentence. However, there exists a paradox in that constructing a datase
t for creating a QE model requires non-trivial human labor and time, and it may even requires additional effort compared to the cost of constructing a parallel corpus. In this study, to address this paradox and utilize the various applications of QE, even in low-resource languages (LRLs), we propose a method for automatically constructing a pseudo-QE dataset without using human labor. We perform a comparative analysis on the pseudo-QE dataset using multilingual pre-trained language models. As we generate the pseudo dataset, we conduct experiments using various external machine translators as test sets to verify the accuracy of the results objectively. Also, the experimental results show that multilingual BART demonstrates the best performance, and we confirm the applicability of QE in LRLs using pseudo-QE dataset construction methods.
This paper describes POSTECH's quality estimation systems submitted to Task 2 of the WMT 2021 quality estimation shared task: Word and Sentence-Level Post-editing Effort. We notice that it is possible to improve the stability of the latest quality es
timation models that have only one encoder based on the self-attention mechanism to simultaneously process both of the two input data, a source sequence and its machine translation, in that such models have neglected to take advantage of pre-trained monolingual representations, which are generally accepted as reliable representations for various natural language processing tasks. Therefore, our model uses two pre-trained monolingual encoders and then exchanges the information of two encoded representations through two additional cross attention networks. According to the official leaderboard, our systems outperform the baseline systems in terms of the Matthews correlation coefficient for machine translations' word-level quality estimation and in terms of the Pearson's correlation coefficient for sentence-level quality estimation by 0.4126 and 0.5497 respectively.
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