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- How to Fine-Tune BERT for Text Classification?1,861
A general solution for BERT fine-tuning is provided and new state-of-the-art results on eight widely-studied text classification datasets are obtained.
- Pre-trained models for natural language processing: A survey1,752
This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.
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- Adversarial Multi-task Learning for Text Classification666
This paper proposes an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other, and shows that the shared knowledge learned can be regarded as off-the-shelf knowledge and easily transferred to new tasks.
- The rise and potential of large language model based agents: a survey656
A comprehensive survey on LLM-based agents is conducted, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based agents.
- 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.
- Extractive Summarization as Text Matching529
This paper forms the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be matched in a semantic space to create a semantic matching framework.
- A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models504
The extensive experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve, especially with the introduction of the RLHF training strategy, and indicates that there is still room for improvement in areas such as model robustness.
- FLAT: Chinese NER Using Flat-Lattice Transformer459
Experiments show FLAT outperforms other lexicon-based models in performance and efficiency and can fully leverage the lattice information and has an excellent parallel ability.
- Adaptive Co-attention Network for Named Entity Recognition in Tweets436
A bi-directional long short term memory network with conditional random fields and an adaptive co-attention network is extended to achieve named entity recognition for tweets to make full use of textual and visual information.
- Phrase dependency parsing for opinion mining435
A novel approach for mining opinions from product reviews is presented, where it converts opinion mining task to identify product features, expressions of opinions and relations between them by taking advantage of the observation that a lot of product features are phrases.
- Simplify the Usage of Lexicon in Chinese NER311
This work proposes a simple but effective method for incorporating the word lexicon into the character representations of Chinese named entity recognition using word lexicons, and shows that the proposed method can be easily incorporated with pre-trained models like BERT.
- Heterogeneous Graph Neural Networks for Extractive Document Summarization306
This paper presents a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences that act as the intermediary between sentences and enrich the cross-sentence relations.
- Long Short-Term Memory Neural Networks for Chinese Word Segmentation293
A novel neural network model for Chinese word segmentation is proposed, which adopts the long short-term memory (LSTM) neural network to keep the previous important information in memory cell and avoids the limit of window size of local context.
- Secrets of RLHF in Large Language Models Part I: PPO290
The PPO-max, an advanced version of PPO algorithm, is explored, to efficiently improve the training stability of the policy model, and a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT is performed.
- GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge289
This paper construct context-gloss pairs and propose three BERT based models for WSD and fine-tune the pre-trained BERT model to achieve new state-of-the-art results on WSD task.
- Memory in the Age of AI Agents288
This work aims to provide an up-to-date landscape of current agent memory research, clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering, and articulate a forward-looking perspective on emerging research frontiers.
- Convolutional neural tensor network architecture for community-based question answering279
This paper proposes a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer, which outperforms the other methods on two matching tasks.
- CNN-Based Chinese NER with Lexicon Rethinking274
This work proposes a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism that can model all the characters and potential words that match the sentence in parallel.
- Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction265
Experiments on the data collected from stock market in Mainland China show that the representation learned from the model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.
- A Lexicon-Based Graph Neural Network for Chinese NER256
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency.
- CoLAKE: Contextualized Language and Knowledge Embedding228
The Contextualized Language and Knowledge Embedding (CoLAKE) is proposed, which jointly learns contextualized representation for both language and knowledge with the extended MLM objective, and achieves surprisingly high performance on a synthetic task called word-knowledge graph completion, which shows the superiority of simultaneously contextualizing language andknowledge representation.
- Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation222
The Style Transformer is proposed, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.
- DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services183
Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of the system in serving various users across diverse legal scenarios, and enhances models' ability to access and utilize external legal knowledge.
- Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents180
A multi-timescale long short-termmemory (MT-LSTM) neural network to model long texts that can model very long documents as well as short sentences is proposed.
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 2026-10-12. 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.
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