In task-oriented dialogs such as MultiWoZ (Budzianowski et al., 2018), an informative and/or successful system response needs to include necessary key information such as the phone number of a hotel.
Studies have proven that domain bias and label bias exist in different Facial Expression Recognition (FER) datasets, making it hard to improve the performance of a specific dataset by adding other datasets.
Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection.
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation.
1 code implementation • • Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
Training complex dialog generation models on small datasets bears high risk of overfitting.
However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored.
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation tasks.
Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts.
We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers.
Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks.
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning.