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Register a new userTranslation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
الترجمة المعرفة عبر المجال: تكبير الانتباه لمهام تجزئة المجال المتقاطعة غير المنشأة
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توضح طبيعة عدم وجود كلمة أو انعكاس كلمة يمكن أن تشير إلى حدود القطاع أو دلالات الكلمة من صعوبة فهم النص الصيني، كما تكثف الطلب على المعرفة الدلالية على مستوى الكلمات لإنجاز هدف وضع العلامات في مهام التجزئة والصينية. ومع ذلك، بالنسبة لمهام تجزئة المجال الصيني والعلامات الصينية غير الخاضعة للرقابة، يعاني النموذج المدرب على المجال المصدر بشكل متكرر من المعرفة الدلالية ذات مستوى الكلمة الناقص بالمجال المستهدف. لمعالجة هذه المشكلة، نقترح نموذجا جديدا يعتمد على تكبير الانتباه لإدخال معرفة كبيرة عبر المجال عبر نظام الترجمة. تتيح النموذج المقترح الانتباه النموذجي إلى رسم المعرفة عبر المجال المشار إليها بواسطة المحاذاة المتبادلة على مستوى الكلمات الضمنية بين المدخلات والترجمة المقابلة لها. بصرف النظر عن النموذج الذي يتطلب الإدخال عبر اللغات، فإننا نؤسس أيضا نموذجا خارج الرف الذي يهرب من الاعتماد على الترجمات عبر اللغات. توضح التجارب أن اقتراحنا يتقدم بدرجة كبيرة نتائج أحدث النتائج من مجزأة الصينية المجال ومهام وضع العلامات.
The nature of no word delimiter or inflection that can indicate segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks. However, for unsupervised Chinese cross-domain segmenting and labeling tasks, the model trained on the source domain frequently suffers from the deficient word-level semantic knowledge of the target domain. To address this issue, we propose a novel paradigm based on attention augmentation to introduce crucial cross-domain knowledge via a translation system. The proposed paradigm enables the model attention to draw cross-domain knowledge indicated by the implicit word-level cross-lingual alignment between the input and its corresponding translation. Aside from the model requiring cross-lingual input, we also establish an off-the-shelf model which eludes the dependency on cross-lingual translations. Experiments demonstrate that our proposal significantly advances the state-of-the-art results of cross-domain Chinese segmenting and labeling tasks.
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