Search Results for author: Can Xu

Found 42 papers, 15 papers with code

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings

no code implementations28 Jan 2022 Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang

A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field.

Contrastive Learning Sentence Embeddings

Recency Dropout for Recurrent Recommender Systems

no code implementations26 Jan 2022 Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen

Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.

Data Augmentation Recommendation Systems

Multimodal Dialogue Response Generation

no code implementations ACL 2022 Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.

Dialogue Generation Response Generation

Learning to Ground Visual Objects for Visual Dialog

no code implementations Findings (EMNLP) 2021 Feilong Chen, Xiuyi Chen, Can Xu, Daxin Jiang

Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process.

Visual Dialog

Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation

no code implementations NeurIPS 2021 YuFei Wang, Can Xu, Huang Hu, Chongyang Tao, Stephen Wan, Mark Dras, Mark Johnson, Daxin Jiang

Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.

Text Generation

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

1 code implementation ACL 2021 Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Xiubo Geng, Daxin Jiang

Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.

Language Modelling Speaker Identification

Maria: A Visual Experience Powered Conversational Agent

1 code implementation ACL 2021 Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.

Learning Matching Representations for Individualized Organ Transplantation Allocation

1 code implementation28 Jan 2021 Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar

Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.

Representation Learning

Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?

no code implementations19 Nov 2020 Yufan Zhao, Wei Wu, Can Xu

We study knowledge-grounded dialogue generation with pre-trained language models.

Dialogue Generation

StyleDGPT: Stylized Response Generation with Pre-trained Language Models

1 code implementation Findings of the Association for Computational Linguistics 2020 Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li

Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training.

Response Generation

Zero-Resource Knowledge-Grounded Dialogue Generation

1 code implementation NeurIPS 2020 Linxiao Li, Can Xu, Wei Wu, Yufan Zhao, Xueliang Zhao, Chongyang Tao

While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain.

Dialogue Generation

Open Domain Dialogue Generation with Latent Images

no code implementations4 Apr 2020 Ze Yang, Wei Wu, Huang Hu, Can Xu, Wei Wang, Zhoujun Li

Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques.

Dialogue Generation Response Generation +2

Low-Resource Knowledge-Grounded Dialogue Generation

no code implementations ICLR 2020 Xueliang Zhao, Wei Wu, Chongyang Tao, Can Xu, Dongyan Zhao, Rui Yan

In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.

Dialogue Generation Response Generation

THUEE system description for NIST 2019 SRE CTS Challenge

no code implementations25 Dec 2019 Yi Liu, Tianyu Liang, Can Xu, Xianwei Zhang, Xianhong Chen, Wei-Qiang Zhang, Liang He, Dandan song, Ruyun Li, Yangcheng Wu, Peng Ouyang, Shouyi Yin

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge.

Speaker Recognition

Low-Resource Response Generation with Template Prior

1 code implementation IJCNLP 2019 Ze Yang, Wei Wu, Jian Yang, Can Xu, Zhoujun Li

Since the paired data now is no longer enough to train a neural generation model, we consider leveraging the large scale of unpaired data that are much easier to obtain, and propose response generation with both paired and unpaired data.

Response Generation

A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots

no code implementations11 Jun 2019 Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, Rui Yan

We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.

Chatbot

Multiobjective Optimization Training of PLDA for Speaker Verification

2 code implementations25 Aug 2018 Liang He, Xianhong Chen, Can Xu, Jia Liu

Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.

Multiobjective Optimization Text-Independent Speaker Verification

Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection

no code implementations22 Aug 2018 Chongyang Tao, Wei Wu, Can Xu, Yansong Feng, Dongyan Zhao, Rui Yan

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.

Dialogue Generation

Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts

no code implementations19 Jul 2018 Can Xu, Wei Wu, Yu Wu

We study open domain dialogue generation with dialogue acts designed to explain how people engage in social chat.

Dialogue Generation reinforcement-learning +1

Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts

no code implementations ICLR 2018 Wei Wu, Can Xu, Yu Wu, Zhoujun Li

Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data.

Dialogue Generation Response Generation

A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots

no code implementations CL 2019 Yu Wu, Wei Wu, Chen Xing, Can Xu, Zhoujun Li, Ming Zhou

The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context.

Large Margin Discriminant Dimensionality Reduction in Prediction Space

no code implementations NeurIPS 2016 Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos

We argue that the intermediate mapping, e. g. boosting predictor, is preserving the discriminant aspects of the data and by controlling the dimension of this mapping it is possible to achieve discriminant low dimensional representations for the data.

Dimensionality Reduction General Classification +1

Visual Sentiment Prediction with Deep Convolutional Neural Networks

no code implementations21 Nov 2014 Can Xu, Suleyman Cetintas, Kuang-Chih Lee, Li-Jia Li

Images have become one of the most popular types of media through which users convey their emotions within online social networks.

Object Recognition Sentiment Analysis +2

Cannot find the paper you are looking for? You can Submit a new open access paper.