Many intellectual endeavors require mathematical problem solving, but this skill remains
beyond the capabilities of computers. To measure this ability in machine learning models …

The modeling of conversational context plays a vital role in emotion recognition from
conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances …

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained
end-to-end to solve logical reasoning problems presented in natural language? We attempt …

Fair machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …

While designing inductive bias in neural architectures has been widely studied, we
hypothesize that transformer networks are flexible enough to learn inductive bias from …

Currently, the non-transparent computing process of deep learning has become a significant
reason hindering its further development. The Neural-Symbolic (NS) system formed by …

Acid-base dissociation constant (pK a) is a key physicochemical parameter in chemical
science, especially in organic synthesis and drug discovery. Current methodologies for pK a …

Transformer models have recently gained popularity in graph representation learning as
they have the potential to learn complex relationships beyond the ones captured by regular …

As the digitalization process has become more and more important in our daily lives, during
recent decades e-commerce has greatly increased in popularity, becoming increasingly …

Automated theorem provers have traditionally relied on manually tuned heuristics to guide
how they perform proof search. Deep reinforcement learning has been proposed as a way to …