URL: https://proceedings.neurips.cc/paper_files/paper/2015/file/277281aada22045c03945dcb2ca6f2ec-Paper.pdf
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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2015)
/EventType (Poster)
/Description-Abstract (Syntactic constituency parsing is a fundamental problem in naturallanguage processing which has been the subject of intensive researchand engineering for decades\056 As a result\054 the most accurate parsersare domain specific\054 complex\054 and inefficient\056 In this paper we showthat the domain agnostic attention\055enhanced sequence\055to\055sequence modelachieves state\055of\055the\055art results on the most widely used syntacticconstituency parsing dataset\054 when trained on a large synthetic corpusthat was annotated using existing parsers\056 It also matches theperformance of standard parsers when trained on a smallhuman\055annotated dataset\054 which shows that this model is highlydata\055efficient\054 in contrast to sequence\055to\055sequence models without theattention mechanism\056 Our parser is also fast\054 processing over ahundred sentences per second with an unoptimized CPU implementation\056)
/Producer (PyPDF2)
/Title (Grammar as a Foreign Language)
/Date (2015)
/ModDate (D\07220151218141422\05508\04700\047)
/Published (2015)
/Type (Conference Proceedings)
/firstpage (2755)
/Book (Advances in Neural Information Processing Systems 28)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (C\056 Cortes and N\056D\056 Lawrence and D\056D\056 Lee and M\056 Sugiyama and R\056 Garnett and R\056 Garnett)
/Author (Oriol Vinyals\054 \225ukasz Kaiser\054 Terry Koo\054 Slav Petrov\054 Ilya Sutskever\054 Geoffrey Hinton)
/lastpage (2763)
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