330 Results
June 7, 2019
Cycle-Consistency for Robust Visual Question Answering
Computer Vision and Pattern Recognition (CVPR)
Despite significant progress in Visual Question Answering over the years, robustness of today’s VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA models are notoriously brittle to linguistic variations in questions.
By: Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh
Computer Vision 
Facebook AI Research 
Machine Learning 
Natural Language Processing & Speech June 3, 2019
Pay less attention with Lightweight and Dynamic Convolutions
International Conference on Learning Representations (ICLR)
Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results.
By: Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael AuliJune 2, 2019
The emergence of number and syntax units in LSTM language models
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two “number units”.
By: Yair Lakretz, Germán Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, Marco BaroniJune 2, 2019
Simple Attention-Based Representation Learning for Ranking Short Social Media Posts
North American Chapter of the Association for Computational Linguistics (NAACL)
This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple, word-level Siamese architecture augmented with attention-based mechanisms for capturing semantic “soft” matches between query and post tokens.
By: Peng Shi, Jinfeng Rao, Jimmy LinJune 1, 2019
Neural Models of Text Normalization for Speech Applications
Computational Linguistics
Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS).
By: Hao Zhang, Richard Sproat, Axel H. Ng, Felix Stahlberg, Xiaochang Peng, Kyle Gorman, Brian RoarkMay 31, 2019
Abusive Language Detection with Graph Convolutional Networks
North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships.
By: Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina ShutovaMay 12, 2019
Provably Accelerated Randomized Gossip Algorithms
IEEE International Conference on Acoustics, Speech, and Signal Processing
In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem.
By: Nicolas Loizou, Michael Rabbat, Peter RichtárikMay 6, 2019
Selfless Sequential Learning
International Conference on Learning Representations (ICLR)
Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them.
By: Rahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsMay 6, 2019
Environment Probing Interaction Policies
International Conference on Learning Representations (ICLR)
In this work, we propose the “Environment-Probing” Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment’s behavior.
By: Wenxuan Zhou, Lerrel Pinto, Abhinav GuptaMay 6, 2019
Multiple-Attribute Text Rewriting
International Conference on Learning Representations (ICLR)
The dominant approach to unsupervised “style transfer” in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its “style”. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations.
By: Guillaume Lample, Sandeep Subramanian, Eric Michael Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau