URL: https://proceedings.neurips.cc/paper_files/paper/2015/file/f442d33fa06832082290ad8544a8da27-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 (We describe an approach for unsupervised learning of a generic\054 distributed sentence encoder\056 Using the continuity of text from books\054 we train an encoder\055decoder model that tries to reconstruct the surrounding sentences of an encoded passage\056 Sentences that share semantic and syntactic properties are thus mapped to similar vector representations\056 We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training\054 allowing us to expand our vocabulary to a million words\056 After training our model\054 we extract and evaluate our vectors with linear models on 8 tasks\072 semantic relatedness\054 paraphrase detection\054 image\055sentence ranking\054 question\055type classification and 4 benchmark sentiment and subjectivity datasets\056 The end result is an off\055the\055shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice\056 We will make our encoder publicly available\056)
/Producer (PyPDF2)
/Title (Skip\055Thought Vectors)
/Date (2015)
/ModDate (D\07220191111141839\05508\04700\047)
/Published (2015)
/Type (Conference Proceedings)
/firstpage (3294)
/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)
/Author (Ryan Kiros\054 Yukun Zhu\054 Russ R\056 Salakhutdinov\054 Richard Zemel\054 Raquel Urtasun\054 Antonio Torralba\054 Sanja Fidler)
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