Файл:Grammar as a Foreign Language 1412.7449v3.pdf

OriolVinyals∗ Google vinyals@google.com LukaszKaiser∗ Google lukaszkaiser@google.com TerryKoo Google terrykoo@google.com SlavPetrov Google slav@google.com IlyaSutskever Google ilyasu@google.com GeoffreyHinton Google geoffhinton@google.com

Abstract

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, incontrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.]