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Register a new userBioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models
Biocopy: آلية نسخ SPAN المكونة والتشغيل في نماذج SEQ2SEQ
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تحصل آليات النسخ بشكل صريح على الرموز دون تغيير من تسلسل المصدر (الإدخال) لإنشاء تسلسل الهدف (الإخراج) ضمن إطار SEQ2SEQ العصبي.ومع ذلك، فإن معظم آليات النسخ الحالية تفكر فقط في نسخ كلمة واحدة من الجمل المصدر، مما يؤدي إلى فقدان الرموز الأساسية أثناء نسخ يمتد لفترة طويلة.في هذا العمل، نقترح هندسة التوصيل والتشغيل، وهي Biocopy، لتخفيف المشكلة المذكورة أعلاه.على وجه التحديد، في مرحلة التدريب، نقوم ببناء علامة حيوية لكل رمزية وتدريب النموذج الأصلي مع علامات الحيوية بشكل مشترك.في مرحلة الاستدلال، سيتوقع النموذج أولا العلامة الحيوية في كل خطوة زمنية، ثم إجراء استراتيجيات قناع مختلفة استنادا إلى الملصق الحيوي المتوقع لتقليل نطاق توزيعات الاحتمالات على قائمة المفردات.النتائج التجريبية على اثنين من المهام الإدارية المنفصلة تظهر أنهم يتفوقون جميعا على النماذج الأساسية عن طريق إضافة البوغايت لدينا إلى هيكل النموذج الأصلي.
Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework. However, most of the existing copy mechanisms only consider single word copying from the source sentences, which results in losing essential tokens while copying long spans. In this work, we propose a plug-and-play architecture, namely BioCopy, to alleviate the problem aforementioned. Specifically, in the training stage, we construct a BIO tag for each token and train the original model with BIO tags jointly. In the inference stage, the model will firstly predict the BIO tag at each time step, then conduct different mask strategies based on the predicted BIO label to diminish the scope of the probability distributions over the vocabulary list. Experimental results on two separate generative tasks show that they all outperform the baseline models by adding our BioCopy to the original model structure.
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