Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Ting LiuSuggested from co-authorship
- Ming ZhouSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works102 from public data
- CodeBERT: A Pre-Trained Model for Programming and Natural Languages4,421
This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.
- Document Modeling with Gated Recurrent Neural Network for Sentiment Classification1,519
A neural network model is introduced to learn vector-based document representation in a unified, bottom-up fashion and dramatically outperforms standard recurrent neural network in document modeling for sentiment classification.
- Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification1,229
Three neural networks are developed to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions and the performance of SSWE is improved by concatenating SSWE with existing feature set.
- Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification1,074
AdaRNN adaptively propagates the sentiments of words to target depending on the context and syntactic relationships between them and it is shown that AdaRNN improves the baseline methods.
- Aspect Level Sentiment Classification with Deep Memory Network1,041
A deep memory network for aspect level sentiment classification that explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect, and is also fast.
- Effective LSTMs for Target-Dependent Sentiment Classification1,010
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
- scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data682
A pretrained deep neural network-based model, single-cell bidirectional encoder representations from transformers (scBERT), to overcome the challenges of existing annotation methods, and is validated on cell type annotation, novel cell type discovery, robustness to batch effects and model interpretability.
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters648
K-Adapter is proposed, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion and captures richer factual and commonsense knowledge than RoBERTa.
- Improving Question Answering by Commonsense-Based Pre-training489
Results show that incorporating commonsense-based function improves the baseline on three question answering tasks that require commonsense reasoning and leverages useful evidence from an external commonsense knowledge base, which is missing in existing neural network models.
- 400
- Learning Semantic Representations of Users and Products for Document Level Sentiment Classification365
By combining evidence at user-, product and documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.
- 335
- Question Generation for Question Answering299
Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.
- Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge266
A novel end-to-end graph neural model called CompareNet is proposed, which compares the news to the knowledge base (KB) through entities for fake news detection and significantly outperforms state-of-the-art methods.
- ToolACE: Winning the Points of LLM Function Calling253
This paper presents ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data, which leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
- Coooolll: A Deep Learning System for Twitter Sentiment Classification251
A neural network with hybrid loss function 1 is developed to learn SSWE, which encodes the sentiment information of tweets in the continuous representation of words, which can be easily re-implemented with the publicly available sentiment-specific word embedding.
- Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering232
This work proposes to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence, and achieves the state-of-the-art accuracy on the CommonsenseQA dataset.
- Deep learning for sentiment analysis: successful approaches and future challenges207
An overview of the successful deep learning approaches for sentiment analysis tasks is provided, lay out the remaining challenges and provide some suggestions to address these challenges.
- Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach192
The sentiment lexicon (TS-Lex) is built with a dedicated neural architecture and integrates the sentiment information of text into its hybrid loss function for learning sentiment-specific phrase embedding (SSPE).
- Reasoning Over Semantic-Level Graph for Fact Checking191
This work proposes two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet, and is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.
- 183
- 163
- User modeling with neural network for review rating prediction163
The lexical semantic composition models are extended and a userword composition vector model (UWCVM) is introduced, which effectively captures how user acts as a function affecting the continuous word representation.
- CoSQA: 20,000+ Web Queries for Code Search and Question Answering162
A contrastive learning method dubbed CoCLR is introduced to enhance text-code matching, which works as a data augmenter to bring more artificially generated training instances to bring better semantic matching between query and code.
- WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach153
This work conducts a thorough examination of pretrained model based unsupervised sentence embeddings and concludes that an easy whitening-based vector normalization strategy with less than 10 lines of code consistently boosts the performance.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.