Search Results for author: Siqi Bao

Found 18 papers, 10 papers with code

PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling

no code implementations EMNLP (NLP4ConvAI) 2021 Xinxian Huang, Huang He, Siqi Bao, Fan Wang, Hua Wu, Haifeng Wang

Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation.

Response Generation

Towards Boosting the Open-Domain Chatbot with Human Feedback

1 code implementation30 Aug 2022 Hua Lu, Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang

Many open-domain dialogue models pre-trained with social media comments can generate coherent replies but have difficulties producing engaging responses when interacting with real users.


Link the World: Improving Open-domain Conversation with Dynamic Spatiotemporal-aware Knowledge

no code implementations28 Jun 2022 Han Zhou, Xinchao Xu, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Siqi Bao, Fan Wang, Haifeng Wang

Making chatbots world aware in a conversation like a human is a crucial challenge, where the world may contain dynamic knowledge and spatiotemporal state.


Amendable Generation for Dialogue State Tracking

1 code implementation EMNLP (NLP4ConvAI) 2021 Xin Tian, Liankai Huang, Yingzhan Lin, Siqi Bao, Huang He, Yunyi Yang, Hua Wu, Fan Wang, Shuqi Sun

In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.

Dialogue State Tracking Multi-domain Dialogue State Tracking +1

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation

3 code implementations20 Sep 2021 Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, ZhengYu Niu

To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.

Dialogue Generation

A Unified Pre-training Framework for Conversational AI

1 code implementation6 May 2021 Siqi Bao, Bingjin Chen, Huang He, Xin Tian, Han Zhou, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Yingzhan Lin

In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation.

Chatbot Interactive Evaluation of Dialog +1

Learning to Select External Knowledge with Multi-Scale Negative Sampling

1 code implementation3 Feb 2021 Huang He, Hua Lu, Siqi Bao, Fan Wang, Hua Wu, ZhengYu Niu, Haifeng Wang

The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB.

Response Generation

DAL: Dual Adversarial Learning for Dialogue Generation

no code implementations WS 2019 Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, Yong Jiang

DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.

Dialogue Generation Response Generation

Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment

1 code implementation ACL 2019 Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, Hua Wu

In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other.


Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

1 code implementation11 Aug 2018 Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng, Huang He, Hua Wu

In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.

Topic Models

3D Randomized Connection Network with Graph-based Label Inference

no code implementations11 Nov 2017 Siqi Bao, Pei Wang, Tony C. W. Mok, Albert C. S. Chung

In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity.

Image Segmentation Semantic Segmentation

Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation

no code implementations24 Oct 2016 Siqi Bao, Albert C. S. Chung

This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image.

Image Segmentation Semantic Segmentation

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