Author name cluster

Benjamin Piwowarski

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6 papers

2 author rows

ICML Conference 2022 Conference Paper

Generative Cooperative Networks for Natural Language Generation

Sylvain Lamprier
Thomas Scialom
Antoine Chaffin
Vincent Claveau
Ewa Kijak
Jacopo Staiano
Benjamin Piwowarski

Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outputs such as language, optimizing GANs remains an open problem with many instabilities, as no gradient can be properly back-propagated from the discriminator output to the generator parameters. An alternative is to learn the generator network via reinforcement learning, using the discriminator signal as a reward, but such a technique suffers from moving rewards and vanishing gradient problems. Finally, it often falls short compared to direct maximum-likelihood approaches. In this paper, we introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts for the task at hand. We give theoretical guarantees of convergence for our approach, and study various efficient decoding schemes to empirically achieve state-of-the-art results in two main NLG tasks.

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NeurIPS Conference 2021 Conference Paper

To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs

Thomas Scialom
Paul-Alexis Dray
Jacopo Staiano
Sylvain Lamprier
Benjamin Piwowarski

Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harder setting than for continuous tasks, which enjoy gradient flows from discriminators to generators, usually leading to dramatic learning instabilities. However, we claim that this can be solved by making discriminator and generator networks cooperate to produce output sequences during training. These cooperative outputs, inherently built to obtain higher discrimination scores, not only provide denser rewards for training but also form a more compact artificial set for discriminator training, hence improving its accuracy and stability. In this paper, we show that our SelfGAN framework, built on this cooperative principle, outperforms Teacher Forcing and obtains state-of-the-art results on two challenging tasks, Summarization and Question Generation.

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NeurIPS Conference 2020 Conference Paper

ColdGANs: Taming Language GANs with Cautious Sampling Strategies

Thomas Scialom
Paul-Alexis Dray
Sylvain Lamprier
Benjamin Piwowarski
Jacopo Staiano

Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences that lack of coherence, factualness, and are prone to repetitions. At the root of these limitations is the mismatch between training and inference, i. e. the so-called exposure bias. Another problem lies in considering only the reference text as correct, while in practice several alternative formulations could be as good. Generative Adversarial Networks (GANs) could mitigate those limitations. Nonetheless, the discrete nature of text has hindered their application to language generation: the approaches proposed so far, based on Reinforcement Learning, have been shown to under-perform MLE. In this context, the exploration is known to be critical, while surprisingly being under-studied. In this work, we show how the most popular sampling method results in unstable training for language GANs. We propose alternative exploration strategies that we named Cold-GANs. By forcing the sampling to be close to the distribution mode, the learning dynamic becomes smoother. We report experimental results obtained on three tasks: unconditional text generation, question generation, and abstractive summarization. For the first time, to the best of our knowledge, the proposed language GANs compare favorably to MLE, and obtain improvements over the state-of-the-art on the considered tasks.

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ICML Conference 2020 Conference Paper

Discriminative Adversarial Search for Abstractive Summarization

Thomas Scialom
Paul-Alexis Dray
Sylvain Lamprier
Benjamin Piwowarski
Jacopo Staiano

We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics. Inspired by Generative Adversarial Networks (GANs), wherein a discriminator is used to improve the generator, our method differs from GANs in that the generator parameters are not updated at training time and the discriminator is used to drive sequence generation at inference time. We investigate the effectiveness of the proposed approach on the task of Abstractive Summarization: the results obtained show that a naive application of DAS improves over the state-of-the-art methods, with further gains obtained via discriminator retraining. Moreover, we show how DAS can be effective for cross-domain adaptation. Finally, all results reported are obtained without additional rule-based filtering strategies, commonly used by the best performing systems available: this indicates that DAS can effectively be deployed without relying on post-hoc modifications of the generated outputs.

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ICML Conference 2019 Conference Paper

Context-Aware Zero-Shot Learning for Object Recognition

Éloi Zablocki
Patrick Bordes
Laure Soulier
Benjamin Piwowarski
Patrick Gallinari

Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only intrinsic properties of objects, e. g. their visual appearance, are taken into account while their context, e. g. the surrounding objects in the image, is ignored. Following the intuitive principle that objects tend to be found in certain contexts but not others, we propose a new and challenging approach, context-aware ZSL, that leverages semantic representations in a new way to model the conditional likelihood of an object to appear in a given context. Finally, through extensive experiments conducted on Visual Genome, we show that contextual information can substantially improve the standard ZSL approach and is robust to unbalanced classes.

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AAAI Conference 2018 Conference Paper

Learning Multi-Modal Word Representation Grounded in Visual Context

Éloi Zablocki
Benjamin Piwowarski
Laure Soulier
Patrick Gallinari

Representing the semantics of words is a long-standing problem for the natural language processing community. Most methods compute word semantics given their textual context in large corpora. More recently, researchers attempted to integrate perceptual and visual features. Most of these works consider the visual appearance of objects to enhance word representations but they ignore the visual environment and context in which objects appear. We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embeddings. We explore various choices for what can serve as a visual context and present an end-to-end method to integrate visual context elements in a multimodal skip-gram model. We provide experiments and extensive analysis of the obtained results.

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