Chen Zheng, Quan Guo and Parisa Kordjamshidi. 2020. Cross-Modality Relevance for Reasoning on Language and Vision. ACL-2020.
Chen Zheng, Yu Sun, Shengxian Wan, and Dianhai Yu. 2019. RLTM: An Efficient Neural IR Framework for Long Documents. IJCAI 2019.
Chen Zheng, Shuangfei Zhai, and Zhongfei(Mark) Zhang. 2017. A Deep Learning Approach for Expert Identification in Question Answering Communities. arXiv preprint arXiv: 1711.05350.
Cross-Modality Relevance for Reasoning on Language and Vision.
1. Designing a novel cross-modality relevance model to learn the relevance representation between components of various input modalities.
2. The model includes the higher-order relevance between entity relations in the text and object relations in the image.
3. Dataset: NLVR2 and VQA.
4. Competitive performance on above two language and vision tasks.
5. The paper published in ACL 2020.
6. Paper name: Cross-Modality Relevance for Reasoning on Language and Vision.
Internship in Big Data and Information Retrieval group, JD Inc.
1. Our IR team presented a novel approach called DPSR, which stands for Deep Personalized and Semantic Retrieval.
2. During the internship, I helped the team design and implement the item module of the DPSR retrieval system at the early stage of this project.
3. Dataset: The DPSR model is trained on a data set of 60 days JD user click logs, which contains 5.6 billion sessions.
4. State-of-the-art performance in Offline test (Top-K and AUC) and Online A/B test (UCVR, GMV, QRR).
5. The paper published in SIGIR 2020. My name appears in Acknowledgement section.
6. Paper name: Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning.
Internship in NLP group, Baidu Inc.
1. Using Deep Learning based semantic matching method to solve Learn-to-Rank problem.
2. Dataset: Baidu Clickthrough dataset.
3. State-of-the-art performance in NDCG and MAP.
4. Paper published in IJCAI 2019.
5. Paper name: RLTM: An Efficient Neural IR Framework for Long Documents.
A Deep Learning Approach for Expert Identification in Question Answering Communities.
1. Building up a language model to implement expert identification in QA communities.
2. Natural Language Processing technologies, such as Word2vec, Glove, DeepWalk, and some Deep Learning technologies, such as Convolutional neural network, Recurrent neural network.
3. Dataset: Stack Overflow community, Zhihu question-answering community.
4. The top-1 test accuracy outperforms all of the baselines.
5. Paper published in arXiv: 1711.05350.
Design and Development of Pet Shop Trade System Based on Java Web.
2. Backend Design and implementation with Struts2, Hibernate, Spring3.
3. Relational Database: MySQL, NoSql Database: Redis
4. Full-Stack design and implementation of recommendation system(Mahout) and search engine(Lucene).
5. Introduced open platform, which can log into the account via Facebook and Tencent account.