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Register a new userA Modest Pareto Optimisation Analysis of Dependency Parsers in 2021
تحليل تحسين باريتو متواضع لمحلل التبعية في عام 2021
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نقيم ثلاثة أنظمة محلل التبعية الرائدة من النماذج المختلفة في مجموعة فرعية صغيرة متناثرة من اللغات من حيث أمامي باريتو الكفاءة من دقتها.نظرا لأننا مهتمون بالكفاءة، فإننا نقيم المحللين الأساسيين دون نماذج لغة محددة (لأن هذه شبكات ضخمة وعادة ما تشكل معظم الوقت لحساب الوقت) أو غيرها من التعزيزات التي يمكن تطبيقها على أي منهم.تظهر تحليل BiAffine كاختيار افتراضي متوازن، مع تحليل وضع العلامات على التسلسل هو الأفضل إذا كانت سرعة الاستدلال (ولكن لا تكلفة الطاقة التدريبية) هي الأولوية.
We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.
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