Search Results for author: Rui Yan

Found 100 papers, 30 papers with code

Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation

no code implementations CCL 2020 Lin Wang, Juntao Li, Rui Yan, Dongyan Zhao

Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.

Story Generation

Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training

no code implementations EMNLP 2020 Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu

Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.

Task-Oriented Dialogue Systems

Stylized Dialogue Generation with Multi-Pass Dual Learning

no code implementations NeurIPS 2021 Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu

Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.

Dialogue Generation

Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

1 code implementation ACL 2021 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.

A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations

no code implementations ACL 2021 Chongyang Tao, Changyu Chen, Jiazhan Feng, Ji-Rong Wen, Rui Yan

Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.

Language Modelling

Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study

no code implementations NAACL 2021 Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan

Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.

Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation

1 code implementation30 May 2021 Shuhe Wang, Yuxian Meng, Xiaofei Sun, Fei Wu, Rongbin Ouyang, Rui Yan, Tianwei Zhang, Jiwei Li

Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context.

Learning to Express in Knowledge-Grounded Conversation

no code implementations NeurIPS 2021 Xueliang Zhao, Tingchen Fu, Chongyang Tao, Wei Wu, Dongyan Zhao, Rui Yan

Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.

Dialogue Generation

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

no code implementations17 Mar 2021 Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan

To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).

Representation Learning

How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution

no code implementations10 Mar 2021 Mingfei Guo, Xiuying Chen, Juntao Li, Dongyan Zhao, Rui Yan

Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks.

Deception Detection

OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts

1 code implementation30 Dec 2020 Yuxian Meng, Shuhe Wang, Qinghong Han, Xiaofei Sun, Fei Wu, Rui Yan, Jiwei Li

Based on this dataset, we propose a family of encoder-decoder models leveraging both textual and visual contexts, from coarse-grained image features extracted from CNNs to fine-grained object features extracted from Faster R-CNNs.

Dialogue Generation

The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

no code implementations14 Dec 2020 Mingzhe Li, Xiuying Chen, Min Yang, Shen Gao, Dongyan Zhao, Rui Yan

In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.

Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

1 code implementation14 Dec 2020 Xiuying Chen, Zhi Cui, Jiayi Zhang, Chen Wei, Jianwei Cui, Bin Wang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.

Multi-Task Learning Reading Comprehension

Interactive Fusion of Multi-level Features for Compositional Activity Recognition

1 code implementation10 Dec 2020 Rui Yan, Lingxi Xie, Xiangbo Shu, Jinhui Tang

To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.

Action Recognition

Design and Commissioning of the PandaX-4T Cryogenic Distillation System for Krypton and Radon Removal

no code implementations4 Dec 2020 Xiangyi Cui, Zhou Wang, Yonglin Ju, Xiuli Wang, Huaxuan Liu, Wenbo Ma, Jianglai Liu, Li Zhao, Xiangdong Ji, Shuaijie Li, Rui Yan, Haidong Sha, Peiyao Huang

An online cryogenic distillation system for the removal of krypton and radon from xenon was designed and constructed for PandaX-4T, a highly sensitive dark matter detection experiment.

Instrumentation and Detectors High Energy Physics - Experiment

Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model

1 code implementation23 Nov 2020 Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan

Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.

Language Modelling Mutual Information Estimation +1

Meaningful Answer Generation of E-Commerce Question-Answering

no code implementations14 Nov 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.

Question Answering Text Generation

Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog

no code implementations5 Nov 2020 Shen Gao, Xiuying Chen, Li Liu, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

1 code implementation EMNLP 2020 Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.

Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues

no code implementations14 Sep 2020 Ruijian Xu, Chongyang Tao, Daxin Jiang, Xueliang Zhao, Dongyan Zhao, Rui Yan

To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.

Conversational Response Selection

Social Adaptive Module for Weakly-supervised Group Activity Recognition

no code implementations ECCV 2020 Rui Yan, Lingxi Xie, Jinhui Tang, Xiangbo Shu, Qi Tian

This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.

Group Activity Recognition

Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response

no code implementations17 Jun 2020 Rui Yan, Xiaoming Duan, Zongying Shi, Yisheng Zhong, Jason R. Marden, Francesco Bullo

With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.

Multi-agent Reinforcement Learning

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

1 code implementation17 May 2020 Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan

We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

Information Retrieval

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

no code implementations10 May 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.

Text Summarization

Epipolar Transformers

1 code implementation CVPR 2020 Yihui He, Rui Yan, Katerina Fragkiadaki, Shoou-I Yu

The intuition is: given a 2D location p in the current view, we would like to first find its corresponding point p' in a neighboring view, and then combine the features at p' with the features at p, thus leading to a 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p'.

Ranked #2 on 3D Human Pose Estimation on Human3.6M (using extra training data)

3D Human Pose Estimation 3D Pose Estimation +1

EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation

no code implementations30 Apr 2020 Jiayi Zhang, Chongyang Tao, Zhenjing Xu, Qiaojing Xie, Wei Chen, Rui Yan

Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.

Language Modelling Short-Text Conversation

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

1 code implementation10 Mar 2020 Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.

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

Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

no code implementations IJCNLP 2019 Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

no code implementations IJCNLP 2019 Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.

Text Generation

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

1 code implementation ACL 2020 Yiping Song, Zequn Liu, Wei Bi, Rui Yan, Ming Zhang

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.

Dialogue Generation Fine-tuning +2

RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search

no code implementations28 Oct 2019 Xiuying Chen, Daorui Xiao, Shen Gao, Guojun Liu, Wei. Lin, Bo Zheng, Dongyan Zhao, Rui Yan

Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).

Multilingual Dialogue Generation with Shared-Private Memory

no code implementations6 Oct 2019 Chen Chen, Lisong Qiu, Zhenxin Fu, Dongyan Zhao, Junfei Liu, Rui Yan

Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.

Cross-Lingual Transfer Dialogue Generation

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

no code implementations IJCNLP 2019 Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.

Style Transfer Text Style Transfer

Learning from Positive and Unlabeled Data with Adversarial Training

no code implementations25 Sep 2019 Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Haiqing Chen, Dongyan Zhao, Jinwen Ma, Rui Yan

Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.

How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

1 code implementation IJCNLP 2019 Shen Gao, Xiuying Chen, Piji Li, Zhangming Chan, Dongyan Zhao, Rui Yan

There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.

Abstractive Text Summarization

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

1 code implementation22 Aug 2019 Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.

Ranked #12 on Entity Alignment on DBP15k zh-en (using extra training data)

Entity Alignment Entity Embeddings +1

Learning towards Abstractive Timeline Summarization

1 code implementation IJCAI 2019 2019 Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.

Document Summarization Timeline Summarization +1

Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References

no code implementations ACL 2019 Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, Rui Yan

Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.

Dialogue Generation

Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification

no code implementations18 Jun 2019 Xiaoye Tan, Rui Yan, Chongyang Tao, Mingrui Wu

Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part.

Classification General Classification

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

GSN: A Graph-Structured Network for Multi-Party Dialogues

1 code implementation31 May 2019 Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan

Existing neural models for dialogue response generation assume that utterances are sequentially organized.

Product-Aware Answer Generation in E-Commerce Question-Answering

1 code implementation23 Jan 2019 Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan

In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.

Question Answering

Abstractive Text Summarization by Incorporating Reader Comments

no code implementations13 Dec 2018 Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan

To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.

Reader-Aware Summarization

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Language understanding +2

Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations

no code implementations19 Nov 2018 Lili Yao, Ruijian Xu, Chao Li, Dongyan Zhao, Rui Yan

To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence.

CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

1 code implementation14 Nov 2018 Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei LI

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.

Text Generation

Plan-And-Write: Towards Better Automatic Storytelling

1 code implementation14 Nov 2018 Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan

Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.

Story Generation

On the Abstractiveness of Neural Document Summarization

no code implementations EMNLP 2018 Fangfang Zhang, Jin-Ge Yao, Rui Yan

Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries.

Abstractive Text Summarization Document Summarization

Iterative Document Representation Learning Towards Summarization with Polishing

1 code implementation EMNLP 2018 Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.

Extractive Text Summarization Representation Learning

Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability

2 code implementations14 Sep 2018 Lingfei Wu, Ian E. H. Yen, Jie Chen, Rui Yan

We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.

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

Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

no code implementations ACL 2018 Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.

Intent Detection Language understanding +2

Topic-Based Question Generation

no code implementations ICLR 2018 Wenpeng Hu, Bing Liu, Rui Yan, Dongyan Zhao, Jinwen Ma

In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.

Chatbot Question Answering +1

Tree2Tree Learning with Memory Unit

no code implementations ICLR 2018 Ning Miao, Hengliang Wang, Ran Le, Chongyang Tao, Mingyue Shang, Rui Yan, Dongyan Zhao

Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.

Machine Translation Translation

Scale Up Event Extraction Learning via Automatic Training Data Generation

no code implementations11 Dec 2017 Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao

We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.

Event Extraction

Style Transfer in Text: Exploration and Evaluation

2 code implementations18 Nov 2017 Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan

Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.

Style Transfer Text Style Transfer

Diversifying Neural Conversation Model with Maximal Marginal Relevance

no code implementations IJCNLP 2017 Yiping Song, Zhiliang Tian, Dongyan Zhao, Ming Zhang, Rui Yan

However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.

Document Summarization Information Retrieval +1

A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification

no code implementations7 Apr 2017 Yaoyuan Zhang, Zhenxu Ye, Yansong Feng, Dongyan Zhao, Rui Yan

For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification.

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

1 code implementation11 Jan 2017 Chongyang Tao, Lili Mou, Dongyan Zhao, Rui Yan

Open-domain human-computer conversation has been attracting increasing attention over the past few years.

Open-Domain Dialog

Neural Emoji Recommendation in Dialogue Systems

no code implementations14 Dec 2016 Ruobing Xie, Zhiyuan Liu, Rui Yan, Maosong Sun

It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.

General Classification

Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

1 code implementation23 Oct 2016 Yiping Song, Rui Yan, Xiang Li, Dongyan Zhao, Ming Zhang

In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.

Dialogue Session Segmentation by Embedding-Enhanced TextTiling

no code implementations13 Oct 2016 Yiping Song, Lili Mou, Rui Yan, Li Yi, Zinan Zhu, Xiaohua Hu, Ming Zhang

In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.

Word Embeddings

StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation

no code implementations15 Apr 2016 Xiang Li, Lili Mou, Rui Yan, Ming Zhang

In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.

How Transferable are Neural Networks in NLP Applications?

no code implementations EMNLP 2016 Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, Zhi Jin

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.

Transfer Learning

Backward and Forward Language Modeling for Constrained Sentence Generation

no code implementations21 Dec 2015 Lili Mou, Rui Yan, Ge Li, Lu Zhang, Zhi Jin

Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants.

Language Modelling Machine Translation +3

Automatic Subspace Learning via Principal Coefficients Embedding

no code implementations17 Nov 2014 Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan

In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).

Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

no code implementations22 Sep 2014 Xi Peng, Rui Yan, Bo Zhao, Huajin Tang, Zhang Yi

Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.

Classification General Classification +2

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