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Register a new userEnd-to-end mBERT based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge
محلل التبعية المعزز للمبرق المحسن في نهاية المطاف مع المعرفة المعززة بالطباعة اللغوية
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نحن تصف محلول Nuig لمهمة IWPT 2021 بمهمة التعبير المعزز (ED) معزز بلغات متعددة.بالنسبة لهذه المهمة المشتركة، نقترح وتقييم محلل إد المحلي المستند SEQ2SEQ SEQ2SEQ ومقرها SEQ2SEQ الذي يتنبأ بمجموعة ED-Parse من جملة مدخلات معينة كأسلسلة موضعية موضعية للنموذج النسبي.نموذجنا المقترح هو شبكة عصبية متعددة الاستخدامات تؤدي خمس مهام رئيسية في وقت واحد وهي وضع علامات UPOS، ووضع العلامات UFEAT، والليمون، والتحليل التبعية والحد من التحليل.علاوة على ذلك، نستخدم النموذج اللغوي المتاح في قاعدة بيانات Wals لتحسين قدرة محللنا المحترفين المقترحين على الانتقال عبر اللغات.تشير النتائج إلى أن SEQ2SEQ ED-Parser المقترح لدينا يؤدي على قدم المساواة مع محلل ED-Art-Art على الرغم من وجود علامة أبسط.
We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given input sentence as a relative head-position tag-sequence. Our proposed model is a multitasking neural-network which performs five key tasks simultaneously namely UPOS tagging, UFeat tagging, Lemmatization, Dependency-parsing and ED-parsing. Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages. Results show that our proposed Seq2seq ED-parser performs on par with state-of-the-art ED-parser despite having a much simpler de- sign.
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