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Found 13 papers, 1 papers with code
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features.
Are Code Pre-trained Models Powerful to Learn Code Syntax and Semantics?
To further understand the code features learnt by these models, in this paper, we target two well-known representative code pre-trained models (i. e., CodeBERT and GraphCodeBERT) and devise a set of probing tasks for the syntax and semantics analysis.
This joke is [MASK]: Recognizing Humor and Offense with Prompting
Humor is a magnetic component in everyday human interactions and communications.
Modular and Parameter-Efficient Multimodal Fusion with Prompting
Recent research has made impressive progress in large-scale multimodal pre-training.
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers.
Discrete and Soft Prompting for Multilingual Models
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs).
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
Continual Learning for Natural Language Generation in Task-oriented Dialog Systems
Natural language generation (NLG) is an essential component of task-oriented dialog systems.
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning.
Quantifying the Contextualization of Word Representations with Semantic Class Probing
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction
We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world{'}s languages.
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable
A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method.
Embedding Learning Through Multilingual Concept Induction
We present a new method for estimating vector space representations of words: embedding learning by concept induction.
