Autoregressive language models are trained by minimizing the cross-entro...
Real-life multilingual systems should be able to efficiently incorporate...
The use of NLP in the realm of financial technology is broad and complex...
Face recognition has made tremendous progress in recent years due to the...
NLP systems typically require support for more than one language. As
dif...
Pretrained multilingual encoders enable zero-shot cross-lingual transfer...
Zero-shot cross-lingual information extraction (IE) describes the
constr...
The goal of generative phonology, as formulated by Chomsky and Halle (19...
Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised
mul...
Pretrained contextualized text encoders are now a staple of the NLP
comm...
A broad goal in natural language processing (NLP) is to develop a system...
The transformer has been shown to outperform recurrent neural network-ba...
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingl...
This work treats the paradigm discovery problem (PDP), the task of learn...
We study the problem of multilingual masked language modeling, i.e. the
...
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual...
We present a study of morphological irregularity. Following recent work,...
Many common character-level, string-to-string transduction tasks, e.g.,
...
Pretrained contextual representation models (Peters et al., 2018; Devlin...
English verbs have multiple forms. For instance, talk may also appear as...
Character-level string-to-string transduction is an important component ...