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Register a new userBacktranslation in Neural Morphological Inflection
خلفية في الانعطاف المورفولوجي العصبي
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Backtranslation هي تقنية شائعة للاستفادة من البيانات غير المسبقة في سيناريوهات الموارد المنخفضة في الترجمة الآلية.تنطبق الطريقة بشكل مباشر على توليد الانفعال المورفولوجي إذا كانت نماذج الكلمة غير المسبقة متوفرة.تقوم هذه الورقة بتقييم إمكانات خلفية الانعطاف المورفولوجي باستخدام البيانات من ست لغات مع البيانات المسمى المسجلة من مورد Sigmorphon المشترك للبيانات والبيانات غير المسبقة من مصادر مختلفة.النتيجة الناتجة الأساسية هي أن Backtranslation يمكن أن تقدم تحسينات متواضعة في سيناريوهات الموارد المنخفضة، ولكن فقط إذا كانت البيانات غير المسبقة نظيفة للغاية وقد تم تصفيتها بنفس المعايير التوضيحية مثل البيانات المسمى.
Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation. The method is directly applicable to morphological inflection generation if unlabeled word forms are available. This paper evaluates the potential of backtranslation for morphological inflection using data from six languages with labeled data drawn from the SIGMORPHON shared task resource and unlabeled data from different sources. Our core finding is that backtranslation can offer modest improvements in low-resource scenarios, but only if the unlabeled data is very clean and has been filtered by the same annotation standards as the labeled data.
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This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization across languages.
Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constrai
nt terms are provided as lemmas. In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. We explore linguistically motivated rule-based and data-driven neural-based inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more accurately than a neural module and outperforms the existing end-to-end approach with lower training costs.
We describe our two NMT systems submitted to the WMT2021 shared task in English-Czech news translation: CUNI-DocTransformer (document-level CUBBITT) and CUNI-Marian-Baselines. We improve the former with a better sentence-segmentation pre-processing a
nd a post-processing for fixing errors in numbers and units. We use the latter for experiments with various backtranslation techniques.
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tu
ned on each language family and finally fine-tuned on individual languages. We use a different type of data augmentation technique in the first two steps. Our system outperformed the only other submission. Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages. We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect. Our code is publicly available.
Morphological rules with various levels of specificity can be learned from example lexemes by recursive application of minimal generalization (Albright and Hayes, 2002, 2003).A model that learns rules solely through minimal generalization was used to
predict average human wug-test ratings from German, English, and Dutch in the SIGMORPHON-UniMorph 2021 Shared Task, with competitive results. Some formal properties of the minimal generalization operation were proved. An automatic method was developed to create wug-test stimuli for future experiments that investigate whether the model's morphological generalizations are too minimal.
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