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Found 10 papers, 2 papers with code
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
Besides, the robustness of the previous neural methods is limited by the large-scale annotated data.
Self-Supervised Quality Estimation for Machine Translation
Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT.
How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph development.
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and Hierarchy-Aware Reranking, which makes retrieval results more concentrated for single-tool queries and more diverse for multi-tool queries.
Improving Cross-lingual Representation for Semantic Retrieval with Code-switching
Semantic Retrieval (SR) has become an indispensable part of the FAQ system in the task-oriented question-answering (QA) dialogue scenario.
Budget-Constrained Tool Learning with Planning
Despite intensive efforts devoted to tool learning, the problem of budget-constrained tool learning, which focuses on resolving user queries within a specific budget constraint, has been widely overlooked.
Black-box Prompt Tuning with Subspace Learning
Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large Language Models (LLMs).
MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images
In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique. Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly. Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies. By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications.
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification.
Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval
Unexpectedly, MLM ignores the sentence-level training, and CL also neglects extraction of the internal info from the query.
