Autoregressive language models are trained by minimizing the cross-entro...
To ensure the fairness of machine learning systems, we can include a fai...
The use of NLP in the realm of financial technology is broad and complex...
Traditional multi-task learning architectures train a single model acros...
Stressors are related to depression, but this relationship is complex. W...
Pretrained multilingual encoders enable zero-shot cross-lingual transfer...
Social media allows researchers to track societal and cultural changes o...
Self-disclosed mental health diagnoses, which serve as ground truth
anno...
Expressing natural language descriptions of structured facts or relation...
Clinical notes in Electronic Health Records (EHR) present rich documente...
Zero-shot cross-lingual information extraction (IE) describes the
constr...
Commonly-used transformer language models depend on a tokenization schem...
Entity linking – the task of identifying references in free text to rele...
Machine learning models that offer excellent predictive performance ofte...
Neural topic models can augment or replace bag-of-words inputs with the
...
Multiple studies have demonstrated that behavior on internet-based socia...
Language varies across users and their interested fields in social media...
Drawing causal conclusions from observational data requires making
assum...
The identification and removal/replacement of protected information from...
Data-driven methods for mental health treatment and surveillance have be...
Cross-language entity linking grounds mentions in multiple languages to ...
The #MeToo movement on Twitter has drawn attention to the pervasive natu...
Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised
mul...
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingl...
Named-entities are inherently multilingual, and annotations in any given...
Computational social science studies often contextualize content analysi...
Clinical notes contain an extensive record of a patient's health status,...
Pretrained contextual representation models (Peters et al., 2018; Devlin...
Causal understanding is essential for many kinds of decision-making, but...
Technical and fundamental analysis are traditional tools used to analyze...
Hand-engineered feature sets are a well understood method for creating r...
This work presents a systematic theoretical and empirical comparison of ...
Many domain adaptation approaches rely on learning cross domain shared
r...
Data on human spatial distribution and movement is essential for
underst...
Modern NLP models rely heavily on engineered features, which often combi...
Named entity recognition, and other information extraction tasks, freque...
We show how to train the fast dependency parser of Smith and Eisner (200...
Most work on building knowledge bases has focused on collecting entities...
Compositional embedding models build a representation (or embedding) for...