Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Localizing a semantic parser to support new languages requires effective cross-lingual
generalization. Recent work has found success with machine-translation or zero-shot …

Prior research has investigated the impact of various linguistic features on cross-lingual
transfer performance. In this study, we investigate the manner in which this effect can be …

Learning with small data is the challenge facing low-resource domains with insufficient data
to perform the target tasks. Among the state-of-the-art approaches in deep learning and …

In this paper, we propose a meta-optimizer-based approach for probabilistic wind power
forecasting (WPF) with limited historical data, including offline training and online adaptation …

The ability to predict changes in binding free energy (▵▵ $ G_ {bind} $) for mutations at
protein-protein interfaces (PPIs) is critical for the understanding genetic diseases and …

Sexism detection remains as an extremely low-resource task for most of the languages
including Chinese. To address this issue, we propose a zero-shot cross-lingual method to …

Semantic parsing maps natural language utterances to logical form representations of
meaning (eg, lambda calculus or SQL). A semantic parser functions as a human-computer …