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Found 15 papers, 6 papers with code
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary.
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity.
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
We demonstrate that TRACE could enhance the entanglement of each segment and preceding latent variables and deduce a non-zero lower bound of the KL term, providing a theoretical guarantee of generation diversity.
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Warning: this paper contains model outputs exhibiting offensiveness and biases.
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks.
CCPM: A Chinese Classical Poetry Matching Dataset
We hope this dataset can further enhance the study on incorporating deep semantics into the understanding and generation system of Chinese classical poetry.
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Ranked #1 on
Grammatical Error Detection
on FCE
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space
Though recent neural models make prominent progress in some criteria of poetry quality, generated poems still suffer from the problem of poor diversity.
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement
The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing.
Automatic Poetry Generation with Mutual Reinforcement Learning
Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood.
Chinese Poetry Generation with a Working Memory Model
Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory.
Chinese Poetry Generation with a Salient-Clue Mechanism
As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations.
Generating Chinese Classical Poems with RNN Encoder-Decoder
We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input.
