Generative Adversarial Networks

method ID: generativeadversarialnetworks

Related Papers

Evaluating Text GANs as Language Models

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractGenerative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle …...

22052

A Preliminary Exploration of GANs for Keyphrase Generation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and …...

29810

Adversarial Grammatical Error Correction

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractRecent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art …...

32205

From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe present a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. Most systems for this task, which can be viewed as monolingual machine translation (MT), …...

32420

Improving Adversarial Text Generation with n-Gram Matching

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

In the past few years, generative adversarial networks (GANs) have become increasingly important in natural language generation. How- ever, their performance seems to still have a sig- nificant margin for …...

29376

PS-GAN: Feature augmented text generation in Telugu

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Since the inception of Generative Adver- sarial Networks (GANs), synthetic im- age generation has taken a giant leap because of the ability of these networks to generate high-quality images, however, …...

29318

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the gen- eration stages …...

33756

Exploring How Generative Adversarial Networks Learn Phonological Representations

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper explores how Generative Adversarial Networks (GANs) learn representations of phonological phenomena. We analyze how GANs encode contrastive and non-contrastive nasality in French and English vowels by applying the …...

38682

GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractPre-trained models have achieved remarkable success in natural language processing (NLP). However, existing pre-training methods underutilize the benefits of language understanding for generation. Inspired by the idea of Generative Adversarial …...

38254