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28 Results

May 7, 2018

Advances in Pre-Training Distributed Word Representations

Language Resources and Evaluation Conference (LREC)

In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together.

By: Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
April 30, 2018

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

International Conference on Learning Representations (ICLR)

In this work we propose an interactive learning procedure called Mechanical Turker Descent (MTD) and use it to train agents to execute natural language commands grounded in a fantasy text adventure game. In MTD, Turkers compete to train better agents in the short term, and collaborate by sharing their agents’ skills in the long term.

By: Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 15, 2018

Learning Filterbanks from Raw Speech for Phone Recognition

International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We train a bank of complex filters that operates on the raw waveform and is fed into a convolutional neural network for end-to-end phone recognition.

By: Neil Zeghidour, Nicolas Usunier, Iasonas Kokkinos, Thomas Schatz, Gabriel Synnaeve, Emmanuel Dupoux
April 15, 2018

Towards End-to-End Spoken Language Understanding

International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)

Spoken language understanding system is traditionally designed as a pipeline of a number of components.

By: Dmitriy Serdyuk, Yongqiang Wang, Christian Fuegen, Anuj Kumar, Baiyang Liu, Yoshua Bengio
October 22, 2017

Inferring and Executing Programs for Visual Reasoning

International Conference on Computer Vision (ICCV)

Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer.

By: Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, Larry Zitnick, Ross Girshick
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.

By: Satwik Kottur, José M.F. Moura, Stefan Lee, Dhruv Batra
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.

By: Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou
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.

By: Holger Schwenk, Matthijs Douze
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.

By: Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov
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.

By: Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes