This page shows the most relevant public items for Machine Learning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Mar 22, 2015 • arxiv.org • Manaal Faruqui, Jesse Dodge, Sujay K. Jauhar, Chris Dyer, Eduard Hovy, Noah A. Smith
Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable informati...
Paper • Feb 15, 2014 • arxiv.org • Yoav Goldberg, Omer Levy
The word2vec software of Tomas Mikolov and colleagues (https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models ...
Paper • Nov 4, 2015 • arxiv.org • Andrew M. Dai, Quoc V. Le
We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional langu...
Paper • Oct 8, 2016 • arxiv.org • Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean
Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems...
Paper • Mar 2, 2016 • arxiv.org • Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging sem...
Paper • Jun 19, 2017 • arxiv.org • Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of wo...
Paper • Aug 9, 2016 • arxiv.org • Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of a...
Paper • Mar 4, 2016 • arxiv.org • John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We compare six compositional arc...
Paper • Sep 27, 2017 • arxiv.org • Rotem Dror, Gili Baumer, Marina Bogomolov, Roi Reichart
With the ever-growing amounts of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure consi...
Paper • Dec 26, 2017 • arxiv.org • Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, w...
Paper • Jul 22, 2017 • arxiv.org • Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the...
Paper • Feb 6, 2017 • openreview.net • Sanjeev Arora, Yingyu Liang, Tengyu Ma
A simple unsupervised method for sentence embedding that can get results comparable to sophisticated models like RNN's and LSTM's
Paper • Feb 15, 2018 • openreview.net • Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
We use the theory of compressed sensing to prove that LSTMs can do at least as well on linear text classification as Bag-of-n-Grams.
Paper • Sep 12, 2018 • arxiv.org • Andreas Rücklé, Steffen Eger, Maxime Peyrard, Iryna Gurevych
Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models such as InferSent. ...
Paper • Jun 20, 2018 • arxiv.org • Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initia...
Paper • Mar 22, 2018 • arxiv.org • Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguist...
Paper • Apr 12, 2018 • arxiv.org • Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse...
Paper • Jun 16, 2018 • arxiv.org • Christian S. Perone, Roberto Silveira, Thomas S. Paula
Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques. In the past years, we saw significant...
Paper • May 24, 2019 • arxiv.org • Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designe...
Paper • Sep 12, 2018 • arxiv.org • Jinman Zhao, Sidharth Mudgal, Yingyu Liang
We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation...
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