Recurrent neural network based language model personalization by social network crowdsourcing
By Tsung-Hsien Wen, A. Heidel, Hung-yi Lee, Yu Tsao, Lin-Shan Lee. 2013
38 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Milica Gasic
- Pei-hao Su
- Tsung-Hsien Wen
- Jianfeng Gao
- Susannah Young
- Stefan Ultes
- Nikola Mrksic
- Bing Liu
In Signal Processing
- Tsung-Hsien Wen
- Susannah Young
- Milica Gasic
- Weinan Zhang
- Pei-hao Su
- Stefan Ultes
- Nikola Mrksic
- Ting-Wei Liu
Citations in the corpus
- A Network-based End-to-End Trainable Task-oriented Dialogue System (2016, background): “Current state-of-the-art belief trackers use discriminative models such as recurrent neural networks (RNN) (Mikolov et al., 2010; Wen et al., 2013) to directly map ASR hypotheses to belief states (Henderson et al.”
- Neural personalized response generation as domain adaptation (2017, background): “– LMP: the state-of-the-art approach for language model personalization, which is proposed by [41].”