Pretrained Language Models for Dialogue Generation with Multiple Input Sources
By Yu Cao, Wei Bi, Meng Fang, Dacheng Tao. 2020
30 citations recorded by the source corpus.
Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.
Subfields: Artificial Intelligence, Natural Language 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
- Yuxiong He
- Samyam Rajbhandari
- Minjia Zhang
- Jie Ren
- Olatunji Ruwase
- Reza Yazdani Aminabadi
- Dan Alistarh
- Elias Frantar
In Natural Language Processing
Citations in the corpus
- ZeRO-Offload: Democratizing Billion-Scale Model Training (2021, methodology): “We perform the evaluation with various batch sizes with 1-billion GPT-2 model on a single GPU.”