1 code implementation • ACL 2022 • Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, Xin Eric Wang
A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks.
1 code implementation • EACL 2021 • Jing Gu, Mostafa Mirshekari, Zhou Yu, Aaron Sisto
Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these.
1 code implementation • 24 Apr 2020 • Jing Gu, Qingyang Wu, Chongruo wu, Weiyan Shi, Zhou Yu
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.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Jing Gu, Zhou Yu
We pro-pose a data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks.
no code implementations • 7 Aug 2020 • Jing Gu, Qingyang Wu, Zhou Yu
Automatic evaluation for open-ended natural language generation tasks remains a challenge.
no code implementations • 14 Oct 2020 • Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu
Transformers have reached remarkable success in sequence modeling.
no code implementations • ACL 2021 • Jing Gu, Qingyang Wu, Chongruo wu, Weiyan Shi, Zhou Yu
However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored.
no code implementations • WNUT (ACL) 2021 • Mostafa Mirshekari, Jing Gu, Aaron Sisto
In this paper, we present Conquest, a framework for generating synthetic datasets for contextual question paraphrasing.
no code implementations • 28 Aug 2022 • Kaizhi Zheng, Kaiwen Zhou, Jing Gu, Yue Fan, Jialu Wang, Zonglin Di, Xuehai He, Xin Eric Wang
Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc.
no code implementations • 7 Mar 2023 • Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
no code implementations • 23 May 2023 • Yue Fan, Jing Gu, Kaizhi Zheng, Xin Eric Wang
Intelligent navigation-helper agents are critical as they can navigate users in unknown areas through environmental awareness and conversational ability, serving as potential accessibility tools for individuals with disabilities.
no code implementations • 29 Jan 2024 • Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Xinze Guan, Xin Eric Wang
Our evaluation shows that questions in the MultipanelVQA benchmark pose significant challenges to the state-of-the-art Large Vision Language Models (LVLMs) tested, even though humans can attain approximately 99\% accuracy on these questions.
1 code implementation • 6 Mar 2024 • Jing Gu, Dongmian Zou
Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks.
no code implementations • 8 Apr 2024 • Jing Gu, Yilin Wang, Nanxuan Zhao, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang, Jianming Zhang, HyunJoon Jung, Xin Eric Wang
Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image.