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Register a new userFrustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing
بسيطة بشكل محبط ولكنه قوي بشكل مدهش: استخدام ميزات مستقلة للغات من أجل التحليل الدلالي الصفر اللغوي
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أدى توفر Corpora إلى تقدم كبير في تدريب المحللين الدلاليين باللغة الإنجليزية.لسوء الحظ، لغات أخرى غير اللغة الإنجليزية، البيانات المشروحة محدودة وكذلك أداء المحللين المتقدمة.لقد أثبتت نماذج متعددة اللغات مؤخرا مفيدة للتحويل الصفر اللغوي في العديد من مهام NLP.ماذا يحتاج إلى تطبيق محلل محلل مدرب باللغة الإنجليزية إلى لغات أخرى من التحليل الدلالي الصفر اللغوي؟هل ستساعد ميزات بسيطة من اللغة المستقلة؟تحقيقا لهذه الغاية، نقوم بتجربة ستة بنية تمثيل خطوبة (DRS) المحللين الدلاليين باللغة الإنجليزية، وتعميمهم إلى الإيطالية والألمانية والهولندية، حيث لا يوجد سوى عدد قليل من الحواجز المشروحة يدويا.تظهر تجارب مكثفة أنه على الرغم من بساطته، مضيفا علاقات التبعية العالمية (UD) وعلامات نقاط البيع العالمية (UPOS) حيث تحقق ميزات نموذجية للأذرع تحسن قوي بشكل مدهش على جميع المحللين.
The availability of corpora has led to significant advances in training semantic parsers in English. Unfortunately, for languages other than English, annotated data is limited and so is the performance of the developed parsers. Recently, pretrained multilingual models have been proven useful for zero-shot cross-lingual transfer in many NLP tasks. What else does it require to apply a parser trained in English to other languages for zero-shot cross-lingual semantic parsing? Will simple language-independent features help? To this end, we experiment with six Discourse Representation Structure (DRS) semantic parsers in English, and generalize them to Italian, German and Dutch, where there are only a small number of manually annotated parses available. Extensive experiments show that despite its simplicity, adding Universal Dependency (UD) relations and Universal POS tags (UPOS) as model-agnostic features achieves surprisingly strong improvement on all parsers.
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Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled corpora, therefore the embeddings of different languages in the models
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