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Found 10 papers, 6 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.
Joint Knowledge Editing for Information Enrichment and Probability Promotion
Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information.
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning
Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance.
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.
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.
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding
Spoken language understanding (SLU) system usually consists of various pipeline components, where each component heavily relies on the results of its upstream ones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
AutoADR: Automatic Model Design for Ad Relevance
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
Improving BERT with Self-Supervised Attention
One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset.
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Learning text representation is crucial for text classification and other language related tasks.
