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Register a new userModeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model
النمذجة التركيز اعتراض الاهتمام للترجمة الآلية العصبية مع نموذج خليط غاوسي
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يعد الانتباه عبر الانتباه عنصرا هاما للترجمة الآلية العصبية (NMT)، والتي تتحقق دائما عن طريق انتباه DOT-Product في الأساليب السابقة.ومع ذلك، فإن اهتمام DOT-Product يعتبر فقط الارتباط بين الكلمات بين الكلمات، مما أدى إلى تشتت عند التعامل مع جمل طويلة وإهمال العلاقات المجاورة للمصدر.مستوحاة من اللغويات، فإن القضايا المذكورة أعلاه ناجمة عن تجاهل نوع من الاهتمام، الذي يطلق عليه الانتباه المركزي، الذي يركز على عدة كلمات مركزية ثم ينتشر حولها.في هذا العمل، نطبق نموذج خليط غاوسي (GMM) لنموذج الاهتمام المركزي بالاهتمام الشامل.تبين التجارب والتحليلات التي أجريناها على ثلاث مجموعات من مجموعات البيانات أن الطريقة المقترحة تتفوق على خط الأساس ولديها تحسن كبير في جودة المحاذاة ودقة N-Gram والترجمة الحكم الطويلة.
Cross-attention is an important component of neural machine translation (NMT), which is always realized by dot-product attention in previous methods. However, dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglect of source neighboring relationships. Inspired by linguistics, the above issues are caused by ignoring a type of cross-attention, called concentrated attention, which focuses on several central words and then spreads around them. In this work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention in cross-attention. Experiments and analyses we conducted on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation.
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