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Found 9 papers, 2 papers with code
Learning Task Sampling Policy for Multitask Learning
It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer.
Methods for Numeracy-Preserving Word Embeddings
We propose a new methodology to assign and learn embeddings for numbers.
Pseudo-OOD training for robust language models
The model is fine-tuned by introducing a new regularization loss that separates the embeddings of IND and OOD data, which leads to significant gains on the OOD prediction task during testing.
Debiasing Gender Bias in Information Retrieval Models
We definitively show that pre-trained models for IR do not perform well in zero-shot retrieval tasks when full fine-tuning of a large pre-trained BERT encoder is performed and that lightweight fine-tuning performed with adapter networks improves zero-shot retrieval performance almost by 20% over baseline.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+1
Improving Downstream Task Performance by Treating Numbers as Entities
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed.
How do lexical semantics affect translation? An empirical study
Neural machine translation (NMT) systems aim to map text from one language into another.
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks.
How much is my car worth? A methodology for predicting used cars prices using Random Forest
Cars are being sold more than ever.
