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Researcher profile

Ludovic Denoyer

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems

    2018

    Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keywordbased queries. We propose a reinforcementlearning-driven translation model framework able to 1) …

  2. Stochastic Adaptive Neural Architecture Search for Keyword Spotting

    2018 · arXiv (Cornell University)

    The problem of keyword spotting i.e. identifying keywords in a real-time audio stream is mainly solved by applying a neural network over successive sliding windows. Due to the difficulty of the task, baseline models are …

  3. A Reinforcement Learning-driven Translation Model for Search-Oriented\n Conversational Systems

    2018 · arXiv (Cornell University)

    Search-oriented conversational systems rely on information needs expressed in\nnatural language (NL). We focus here on the understanding of NL expressions for\nbuilding keyword-based queries. We propose a reinforcement-learning-driven\ntranslation model framework able to 1) learn the translation …

  4. Building a Subspace of Policies for Scalable Continual Learning

    2022 · arXiv (Cornell University)

    The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size …

  5. Word Translation Without Parallel Data

    2017 · arXiv (Cornell University)

    State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed …

  6. Unsupervised Machine Translation Using Monolingual Corpora Only

    2017 · arXiv (Cornell University)

    Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet …

  7. Phrase-Based & Neural Unsupervised Machine Translation

    2018

    Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This …