no code implementations • EMNLP (NLP4ConvAI) 2021 • Shuyang Dai, Guoyin Wang, Sunghyun Park, Sungjin Lee
In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.
no code implementations • 14 Feb 2024 • Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi, Sungroh Yoon
Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content.
no code implementations • 13 Jul 2020 • Miaoyun Zhao, Yulai Cong, Shuyang Dai, Lawrence Carin
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary.
2 code implementations • ICML 2020 • Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin
In this paper, we propose a novel Contrastive Log-ratio Upper Bound (CLUB) of mutual information.
no code implementations • NAACL 2021 • Shuyang Dai, Zhe Gan, Yu Cheng, Chenyang Tao, Lawrence Carin, Jingjing Liu
In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations.
1 code implementation • NeurIPS 2019 • Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy Bobashev, Lawrence Carin
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
no code implementations • 11 Sep 2019 • Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu, Lawrence Carin
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
no code implementations • 5 Jun 2019 • Shuyang Dai, Kihyuk Sohn, Yi-Hsuan Tsai, Lawrence Carin, Manmohan Chandraker
We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation.
1 code implementation • NeurIPS 2018 • Liqun Chen, Shuyang Dai, Chenyang Tao, Dinghan Shen, Zhe Gan, Haichao Zhang, Yizhe Zhang, Lawrence Carin
However, the discrete nature of text hinders the application of GAN to text-generation tasks.
2 code implementations • ICML 2018 • Yunchen Pu, Shuyang Dai, Zhe Gan, Wei-Yao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
2 code implementations • 6 Sep 2017 • Liqun Chen, Shuyang Dai, Yunchen Pu, Chunyuan Li, Qinliang Su, Lawrence Carin
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence.