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Found 8 papers, 7 papers with code
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT variant outperforms the vanilla version, not only on these tasks but also more broadly across a range of Chinese natural language understanding tasks.
TSRankLLM: A Two-Stage Adaptation of LLMs for Text Ranking
Text ranking is a critical task in various information retrieval applications, and the recent success of pre-trained language models (PLMs), especially large language models (LLMs), has sparked interest in their application to text ranking.
Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track
Large-scale text retrieval technology has been widely used in various practical business scenarios.
A Simple but Effective Bidirectional Framework for Relational Triple Extraction
Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Next, the mined global associations are integrated into the table feature of each relation.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
A Conditional Cascade Model for Relational Triple Extraction
Tagging based methods are one of the mainstream methods in relational triple extraction.
An Effective System for Multi-format Information Extraction
Finally, our system ranks No. 4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79. 887%, 85. 179%, and 70. 828% respectively.
