Search Results for author: Cheng Niu

Found 12 papers, 7 papers with code

MovieChats: Chat like Humans in a Closed Domain

no code implementations EMNLP 2020 Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie zhou

In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.

Chatbot

Diversifying Dialogue Generation with Non-Conversational Text

1 code implementation ACL 2020 Hui Su, Xiaoyu Shen, Sanqiang Zhao, Xiao Zhou, Pengwei Hu, Randy Zhong, Cheng Niu, Jie zhou

Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.

Dialogue Generation Translation

Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization

no code implementations26 Apr 2020 Zeyang Lei, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Yujiu Yang, Cheng Niu, Jie zhou

Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard.

Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels

no code implementations21 Apr 2020 Canxiang Yan, Jianhao Yan, Yangyin Xu, Cheng Niu, Jie zhou

Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation.

Graph-to-Sequence Text Generation

Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog

1 code implementation1 Feb 2020 Zekang Li, Zongjia Li, Jinchao Zhang, Yang Feng, Cheng Niu, Jie zhou

Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8).

Dialogue Generation Multi-Task Learning

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension

no code implementations WS 2019 Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.

Reading Comprehension

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations

1 code implementation ACL 2019 Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, Jie zhou

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document.

Improving Multi-turn Dialogue Modelling with Utterance ReWriter

1 code implementation ACL 2019 Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie zhou

To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.

Coreference Resolution Dialogue Rewriting

Cross-Domain Labeled LDA for Cross-Domain Text Classification

1 code implementation16 Sep 2018 Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu

To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA).

Cross-Domain Text Classification General Classification

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