The followings are some of our representative research papers.
Notes: The Language collumn in the following tables indicates that the models are evaluated on those languages in the paper. It does not mean the model will not work on other languages.
| Name | Paper | Code | Language |
|---|---|---|---|
| DSG | Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings | link | Chinese |
| ZEN 1.0 | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | link | Chinese |
| ZEN 2.0 | ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders | link | Arabic, Chinese |
| T-DNA | Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation | link | English |
| 🔥 ChiMed-GPT | ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences | link | Chinese, English |
Model Recommendation: DSG provides 200-dimensional word embeddings for around 8M Chinese words. ZEN 2.0 provides large pre-trained language models (the large version uses 24 layers of self-attentions with 1024 dimensional hidden vectors) for Arabic and Chinese. The models are trained on large corpus and enhance text modeling through n-grams. ChiMed-GPT is a Chinese medical large language model (LLM) built by continually training Ziya-v2 on Chinese medical data, where pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) are comprehensively performed on it.
Model Recommendation: WMSeg and McASP contains easy-to-use CWS and joint CWS and POS tagging models that are based on BERT and ZEN. Models trained on different datasets are available for downloading.
| Name | Paper | Code | Language |
|---|---|---|---|
| SAPar | Improving Constituency Parsing with Span Attention | link | Arabic, Chinese, English |
| DMPar | Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing | link | English |
| NeST-CCG | Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks | link | English |
Model Recommendation: SAPar provides constituent parsers (which are based on BERT, XLNet, and ZEN) for Arabic, Chinese, and English; DMPar provides code for dependency parsing; NeST-CCG offers BERT-based models for English CCG supertagging. Both repositories provide pre-trained models and they are easy-to-use.
| Name | Paper | Code | Language |
|---|---|---|---|
| SRL-MM | Syntax-driven Approach for Semantic Role Labeling | link | English |
| Name | Paper | Code | Language |
|---|---|---|---|
| SANER | Named Entity Recognition for Social Media Texts with Semantic Augmentation | link | Chinese,English |
| AESINER | Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information | link | Chinese,English |
| BioKMNER | Improving biomedical named entity recognition with syntactic information | link | English |
Model Recommendation: SANER use pre-trained language models and word embeddings in text modeling, with the semantic of similar words are used to enhance text understanding. Pre-trained models are available for downloading and they are easy-to-use.
| Name | Paper | Code | Language |
|---|---|---|---|
| Pronoun-Coref-KG | Knowledge-aware Pronoun Coreference Resolution | link | English |
| Pronoun-Coref | Incorporating Context and External Knowledge for Pronoun Coreference Resolution | link | English |
| Visual_PCR | What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues | link | English |
Model Recommendation: Pronoun-Coref uses GloVe and ELMo embeddings in text modeling. The model is light and easy-to-use.
| Name | Paper | Code | Language |
|---|---|---|---|
| ASA-TGCN | Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble | link | English |
| ASA-WD | Enhancing Aspect-level Sentiment Analysis with Word Dependencies | link | English |
| ASA-CLD | Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis | link | English |
| DGSA | Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks | link | English |
| ASA-TM | Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories | link | English |
Model Recommendation: DGSA provides an end-to-end solution (the model are based on BERT) for aspect-level sentiment analysis, which can be directly used to process raw text.
| Name | Paper | Code | Language |
|---|---|---|---|
| RE-AGCN | Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks | link | English |
| RE-TAMM | Relation Extraction with Type-aware Map Memories of Word Dependencies | link | English |
| RE-DMP | Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking | link | English |
| RE-NGCN | Relation Extraction with Word Graphs from N-grams | link | English |
| RE-AMT | Enhancing Relation Extraction via Adversarial Multi-task Learning | link | English |
Model Recommendation: RE-AGCN provides BERT-based models for relation extraction, where the model leverages the auto-parsed dependency tree of the input text to have a better understanding to the text.
| Name | Paper | Code | Language |
|---|---|---|---|
| T-DNA | Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation | link | English |
| SDG4DA | Reinforced Training Data Selection for Domain Adaptation | link | English |
| DPM4DA | Domain Adaptation for Disease Phrase Matching with Adversarial Networks | -- | English |
| TD4DA | Entropy-based Training Data Selection for Domain Adaptation | -- | Chinese, English |
| GM4DA | Using a goodness measurement for domain adaptation: A case study on Chinese word segmentation | -- | Chinese |
Model Recommendation: T-DNA is a Transformer-based language model for domain adaptation, which can be used easily.
| Name | Paper | Code | Language |
|---|---|---|---|
| HET-MC | Summarizing Medical Conversations via Identifying Important Utterances | link | Chinese |
| BioKMNER | Improving biomedical named entity recognition with syntactic information | link | English |
| Name | Paper | Code | Language |
|---|---|---|---|
| R2GenRL | Reinforced Cross-modal Alignment for Radiology Report Generation | link | English |
| R2GenCMN | Cross-modal Memory Networks for Radiology Report Generation | link | English |
| R2Gen | Generating Radiology Reports via Memory-driven Transformer | link | English |
| 🔥RRG-Review | A Systematic Review of Deep Learning-based Research on Radiology Report Generation | -- | English |
| Name | Paper | Code | Language |
|---|---|---|---|
| ChiMed | ChiMed: A Chinese Medical Corpus for Question Answering | link | Chinese |
| ChiMST | ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition | link | Chinese |
| Chinese CCGBank | Chinese CCGBank Construction from Tsinghua Chinese Treebank | -- | Chinese |
| HNZ | The Construction of a Segmented and Part-of-speech Tagged Archaic Chinese Corpus: A Case Study on Huainanzi | link | Chinese |