Question answering on tabular data (a.k.a TableQA), which aims at genera...
Named Entity Recognition (NER) aims to extract and classify entity menti...
The key to the success of few-shot segmentation (FSS) lies in how to
eff...
Position bias, the phenomenon whereby users tend to focus on higher-rank...
Abductive reasoning aims to find plausible explanations for an event. Th...
Explainable multi-hop question answering (QA) not only predicts answers ...
Dynamically typed languages such as Python have become very popular. Amo...
Task-oriented parsing (TOP) aims to convert natural language into
machin...
Few-shot table-to-text generation is a task of composing fluent and fait...
Multimodal sentiment analysis has attracted increasing attention and lot...
Crystal-structure phase mapping is a core, long-standing challenge in
ma...
Understanding how environmental characteristics affect bio-diversity
pat...
Multi-label classification (MLC) is a generalization of standard
classif...
There has been an increasing interest in harnessing deep learning to tac...
The non-autoregressive models have boosted the efficiency of neural mach...
Co-saliency detection aims to detect common salient objects from a group...
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework t...
Numerous pattern recognition applications can be formed as learning from...