GS-2019-018 Processing of heterogeneousdata and its specialized applicationsComputational systems use natural language for communication with humans moreoften in the last years. This work summarises state-of-the-art approaches in thefield of generative models, especially in the text domain. It offers a complex study ofspecific problems known from this domain and related ones like adversarial training,reinforcement learning, artificial neural networks, etc. It also addresses the usageof these models in the context of non-generative approaches and the possibility ofcombining both
International audienceGenerative Adversarial Networks (GANs) have known a tremendous success for man...
We introduce the problems of data-to-text generation and the current state of the art, i.e. pretrain...
This paper proposes a learning-based methodology to deal with Natural Language. Our system tries to ...
Thesis (Ph.D.)--University of Washington, 2020Natural language generation plays an important role in...
Text Generation is a pressing topic of Natural Language Processing that involves the prediction of u...
The development of natural language generation systems has been one of the most considerable subject...
This book constitutes the refereed proceedings of the Third International Conference on Natural Lang...
Text-to-text generation is a fundamental task in natural language processing. Traditional models rel...
The ability to learn robust, resizable feature representations from unlabeled data has potential app...
The research field of Natural Language Generation offers practitioners a wide range of techniques fo...
These improvements open many possibilities in solving Natural Language Processing downstream tasks. ...
Thesis (Master's)--University of Washington, 2020This thesis presents a study that was designed to t...
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often c...
Generative Adversarial Networks (GANs) continue to be one of the most popular deep learning approach...
We study the problem of building generative models of natural source code (NSC); that is, source cod...
International audienceGenerative Adversarial Networks (GANs) have known a tremendous success for man...
We introduce the problems of data-to-text generation and the current state of the art, i.e. pretrain...
This paper proposes a learning-based methodology to deal with Natural Language. Our system tries to ...
Thesis (Ph.D.)--University of Washington, 2020Natural language generation plays an important role in...
Text Generation is a pressing topic of Natural Language Processing that involves the prediction of u...
The development of natural language generation systems has been one of the most considerable subject...
This book constitutes the refereed proceedings of the Third International Conference on Natural Lang...
Text-to-text generation is a fundamental task in natural language processing. Traditional models rel...
The ability to learn robust, resizable feature representations from unlabeled data has potential app...
The research field of Natural Language Generation offers practitioners a wide range of techniques fo...
These improvements open many possibilities in solving Natural Language Processing downstream tasks. ...
Thesis (Master's)--University of Washington, 2020This thesis presents a study that was designed to t...
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often c...
Generative Adversarial Networks (GANs) continue to be one of the most popular deep learning approach...
We study the problem of building generative models of natural source code (NSC); that is, source cod...
International audienceGenerative Adversarial Networks (GANs) have known a tremendous success for man...
We introduce the problems of data-to-text generation and the current state of the art, i.e. pretrain...
This paper proposes a learning-based methodology to deal with Natural Language. Our system tries to ...