Search Results for author: Juntao Li

Found 17 papers, 4 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

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

no code implementations1 Oct 2021 Chongyang Tao, Jiazhan Feng, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.

Re-Ranking

DM-CT: Consistency Training with Data and Model Perturbation

no code implementations29 Sep 2021 Xiaobo Liang, Runze Mao, Lijun Wu, Juntao Li, Weiqing Liu, Qing Li, Min Zhang

The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.

Data Augmentation Image Classification +2

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.

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

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

Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training

2 code implementations EMNLP 2020 Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing

To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.

Fine-tuning Text Classification +1

Solution Path Algorithm for Twin Multi-class Support Vector Machine

no code implementations30 May 2020 Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan, Juntao Li

The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems, however, which is faced with some difficulties such as model selection and solving multi-classification problems quickly.

Classification General Classification +1

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

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

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

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