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Register a new userGeneralised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
تكيف المجال غير المقترح للتعميم من الترجمة الآلية العصبية مع اختيار البيانات عبر اللغات
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تعتبر هذه الورقة مشكلة تكيف المجال غير المدعومة من أجل الترجمة الآلية العصبية (NMT)، حيث نفترض الوصول إلى نص أحادي فقط إما في المصدر أو اللغة المستهدفة في المجال الجديد. نقترح طريقة اختيار البيانات عبر اللغات لاستخراج الجمل داخل المجال في جانب اللغة المفقودة من كوربوس أحادية الأجل عام كبيرة. تقوم طريقةنا المقترحة بتدريب طبقة تكيفية على رأس بيرتف متعدد اللغات من خلال التعلم المتعرج عن تعايز التمثيل بين المصدر واللغة المستهدفة. ثم يتيح ذلك تحويل قابلية تحويل المجال بين اللغات بطريقة طلقة صفرية. بمجرد اكتشاف البيانات داخل المجال من قبل المصنف، يتم بعد ذلك تكييف نموذج NMT بالمجال الجديد من خلال مهام الترجمة التعلم المشتركة ومهام التمييز بين المجال. نقيم طريقة اختيار بياناتنا عبر اللغات لدينا على NMT عبر خمسة مجالات متنوعة في ثلاث أزواج لغوية، وكذلك سيناريو في العالم الحقيقي للترجمة Covid-19. تظهر النتائج أن أسلوبنا المقترح تتفوق على خطوط خطوط خطوط اختيار الاختيار الأخرى تصل إلى +1.5 درجة بلو.
This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score.
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We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues. As a first step, we propose to fragment data into phrase pairs and use a random sampl
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