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Found 8 papers, 4 papers with code
Context-aware Adversarial Attack on Named Entity Recognition
In recent years, large pre-trained language models (PLMs) have achieved remarkable performance on many natural language processing benchmarks.
Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and diversity of training data in low-resource scenarios.
CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection. The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28\% in terms of exact matching of relevant sentences on the HotpotQA dataset.
Data Augmentation for Cross-Domain Named Entity Recognition
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models.
Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp
Performance of neural models for named entity recognition degrades over time, becoming stale.
Can images help recognize entities? A study of the role of images for Multimodal NER
Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context.
