170 citations · 669 across the 14 of their papers we have counts for
18 papers
TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
Yinhong Liu, Yimai Fang, David Vandyke +1
In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across div…
Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer
Benjamin Muller, Deepanshu Gupta, Siddharth Patwardhan +3
Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lin…
Prompting for a conversation: How to control a dialog model?
Josef Valvoda, Yimai Fang, David Vandyke
Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. B…
Plan-then-Generate: Controlled Data-to-Text Generation via Planning
Yixuan Su, David Vandyke, Sihui Wang +2
Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated outpu…
Non-Autoregressive Text Generation with Pre-trained Language Models
Yixuan Su, Deng Cai, Yan Wang +4
Non-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind…
A Generative Model for Joint Natural Language Understanding and Generation
Bo-Hsiang Tseng, Jianpeng Cheng, Yimai Fang +1
Natural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives…