In recent years, conversational agents (CAs) have become ubiquitous and are a presence in
our daily routines. It seems that the technology has finally ripened to advance the use of CAs …

Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark
of human intelligence, it involves a degree of explicit reading comprehension, interpretation …

Automated reasoning with unstructured natural text is a key requirement for many potential
applications of NLP and for develo** robust AI systems. Recently, Language Models …

Logical reasoning of text requires understanding critical logical information in the text and
performing inference over them. Large-scale pre-trained models for logical reasoning mainly …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

In settings from fact-checking to question answering, we frequently want to know whether a
collection of evidence (premises) entails a hypothesis. Existing methods primarily focus on …

Current abstractive summarization models either suffer from a lack of clear interpretability or
provide incomplete rationales by only highlighting parts of the source document. To this end …

Formal verification has emerged as a powerful approach to ensure the safety and reliability
of deep neural networks. However, current verification tools are limited to only a handful of …

We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-
choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of …