October 8, 2017

Living a Discrete Life in a Continuous World: Reference in Cross-Modal Entity Tracking

Proceedings of IWCS (12th International Conference on Computational Semantics)

This paper (a) introduces a concrete referential task to test both aspects, called cross-modal entity tracking; (b) proposes a neural network architecture that uses external memory to build an entity library inspired in the DRSs of DRT, with a mechanism to dynamically introduce new referents or add information to referents that are already in the library.

Gemma Boleda, Sebastian Pado', Nghia The Pham, Marco Baroni
October 5, 2017

STARDATA: a StarCraft AI Research Dataset

Association for the Advancement of Artificial Intelligence Digital Entertainment Conference

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft.

Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve
September 9, 2017

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Conference on Empirical Methods on Natural Language Processing (EMNLP)

In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors (Kiros et al., 2015) on a wide range of transfer tasks.

Alexis Conneau, Douwe Kiela, Holger Schwenk, Loıc Barrault, Antoine Bordes
September 9, 2017

Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection

The Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Marek Rei, Luana Bulat, Douwe Kiela, Ekaterina Shutova
September 7, 2017

Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog

Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional.

Satwik Kottur, José M.F. Moura, Stefan Lee, Dhruv Batra
August 6, 2017

Unsupervised Learning by Predicting Noise

International Conference on Machine Learning (ICML)

Convolutional neural networks provide visual features that perform well in many computer vision applications. However, training these networks requires large amounts of supervision; this paper introduces a generic framework to train such networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them.

Piotr Bojanowski, Armand Joulin
August 6, 2017

Wasserstein Generative Adversarial Networks

International Conference on Machine Learning (ICML)

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.

Martin Arjovsky, Soumith Chintala, Leon Bottou
August 6, 2017

Language Modeling with Gated Convolutional Networks

International Conference on Machine Learning (ICML)

The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens.

Yann Dauphin, Angela Fan, Michael Auli, David Grangier
August 6, 2017

Efficient Softmax Approximation for GPUs

International Conference on Machine Learning (ICML)

We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies.

Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou
August 6, 2017

Convolutional Sequence to Sequence Learning

International Conference on Machine Learning (ICML)

We introduce an architecture based entirely on convolutional neural networks.

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
July 31, 2017

Enriching Word Vectors with Subword Information

TACL, Association for Computational Linguistics (ACL 2017)

In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams.

Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
July 31, 2017

Learning Multilingual Joint Sentence Embeddings with Neural Machine Translation

ACL workshop on Representation Learning for NLP (ACL)

In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics.

Holger Schwenk, Matthijs Douze
July 30, 2017

Reading Wikipedia to Answer Open-Domain Questions

Association for Computational Linguistics (ACL 2017)

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.

Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
July 30, 2017

Automatically Generating Rhythmic Verse with Neural Networks

Association for Computational Linguistics (ACL 2017)

We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms.

Jack Hopkins, Douwe Kiela
July 30, 2017

A Convolutional Encoder Model for Neural Machine Translation

Association for Computational Linguistics 2017

The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers.

Jonas Gehring, Michael Auli, David Grangier, Yann Dauphin
July 22, 2017

Densely Connected Convolutional Networks

CVPR 2017

In this paper, we embrace the observation that hat convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output, and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion.

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
July 21, 2017

Discovering Causal Signals in Images

CVPR 2017

This paper establishes the existence of observable footprints that reveal the “causal dispositions” of the object categories appearing in collections of images.

David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, Leon Bottou
July 21, 2017

Relationship Proposal Networks

Conference on Computer Vision and Pattern Recognition 2017

In this paper we address the challenges of image scene object recognition by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions.

Ahmed Elgammal, Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang
July 21, 2017

Link the head to the “beak”: Zero Shot Learning from Noisy Text Description at Part Precision

CVPR 2017

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. F

Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal
July 21, 2017

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CVPR 2017

We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires.

Bharath Hariharan, Justin Johnson, Larry Zitnick, Laurens van der Maaten, Li Fei-Fei, Ross Girshick