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Found 11 papers, 6 papers with code
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks.
DINOISER: Diffused Conditional Sequence Learning by Manipulating Noises
In this paper, we introduce DINOISER to facilitate diffusion models for sequence generation by manipulating noises.
Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning
Previously, non-autoregressive models were widely perceived as being superior in generation efficiency but inferior in generation quality due to the difficulties of modeling multiple target modalities.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Non-Autoregressive Models are Better Multilingual Translators
Multilingual machine translation aims to develop a single model for multiple language directions.
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.
Non-iterative Parallel Text Generation via Glancing Transformer
Although non-autoregressive models with one-iteration generation achieves remarkable inference speed-up, they still falls behind their autoregressive counterparts inprediction accuracy.
Glancing Transformer for Non-Autoregressive Neural Machine Translation
With GLM, we develop Glancing Transformer (GLAT) for machine translation.
Ranked #68 on
Machine Translation
on WMT2014 English-German
Exploring Diverse Expressions for Paraphrase Generation
Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification.
QA4IE: A Question Answering based Framework for Information Extraction
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts.
