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Found 20 papers, 11 papers with code
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System
Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph.
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages.
SAMP: A Toolkit for Model Inference with Self-Adaptive Mixed-Precision
The latest industrial inference engines, such as FasterTransformer1 and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed.
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models
These features make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism and tensor model parallelism, to achieve high training efficiency.
Semantic Matching from Different Perspectives
In this paper, we pay attention to the issue which is usually overlooked, i. e., \textit{similarity should be determined from different perspectives}.
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.
Approximating Optimal Transport via Low-rank and Sparse Factorization
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.
Dynamic Relevance Learning for Few-Shot Object Detection
By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly.
Efficient Cross-Device Federated Learning Algorithms for Minimax Problems
In many machine learning applications where massive and privacy-sensitive data are generated on numerous mobile or IoT devices, collecting data in a centralized location may be prohibitive.
Whitening Sentence Representations for Better Semantics and Faster Retrieval
Therefore, some attempts of boosting the isotropy of sentence distribution, such as flow-based model, have been applied to sentence representations and achieved some improvement.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network
In this paper, we propose a new few-shot text classification method.
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction
In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence.
Ranked #1 on
Relation Extraction
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FastBERT: a Self-distilling BERT with Adaptive Inference Time
Pre-trained language models like BERT have proven to be highly performant.
A Decentralized Proximal Point-type Method for Saddle Point Problems
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network.
K-BERT: Enabling Language Representation with Knowledge Graph
For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.
