On Variational Learning of Controllable Representations for Text without Supervision

By Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao. 2019

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In Natural Language Processing

Citations in the corpus

A Recipe for Arbitrary Text Style Transfer with Large Language Models (2021, background): “Even bleeding-edge approaches that tackle the challenging problem of label-free style transfer are limited in that they require at least several exemplar sentences that dictate a given target style (Xu et al., 2020; Riley et al., 2021).”
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation (2022, background): “, 2018a), or foregoes training-time labels entirely, as in our setting, relying only on few-shot exemplars provided at inference time (Xu et al., 2020; Riley et al., 2021; Garcia et al., 2021).”
Text Style Transfer: A Review and Experiment Evaluation (2020, methodology): “The scarcity of parallel data motivated a new breed of TST algorithms that attempt to transfer text style without parallel data [9, 11, 15, 18, 20, 22, 24, 29, 40, 41, 44, 46– 48, 54, 60, 67, 70, 72, 76, 78, 81, 82, 85, 86, 88, 90, 92, 93].”
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021, methodology): “We follow the current approach of the community (Yang et al., 2018; Logeswaran et al., 2018; Wang et al., 2019; Xu et al., 2019a; Lample et al., 2019; Dai et al., 2019a; He et al., 2020) and approximate the three criteria with the following metrics: 1.”
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling (2021, methodology): “1Our work is concurrent with Xu et al. (2020), who offer a substantially different approach to label-free style transfer, as discussed in Sections 3 and 5. experiments, the dimensionality of this vector and the encoder hidden states is 1024.”
Deep Learning for Text Style Transfer: A Survey (2020, methodology): “Xu et al. (2020) apply image style transfer to adversarial attack, and future research can also explore the use of TST in the two ways suggested above.”