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Found 7 papers, 1 papers with code
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?
In this work, we evaluate the utility of CPT for generative UDA.
Evolving Domain Adaptation of Pretrained Language Models for Text Classification
Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection.
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection
Fine-tuning with pre-trained language models has been a de facto procedure to derive OOD detectors with respect to in-distribution (ID) data.
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information.
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition
Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks.
LRS-DAG: Low Resource Supervised Domain Adaptation with Generalization Across Domains
Existing non-adversarial methods learn mappings between the source and target domains, to achieve reasonable performance.
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)
Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal.
