The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …

Large language models (LLMs) have shown remarkable reasoning capabilities given chain-
of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

Despite recent improvements in abstractive summarization, most current approaches
generate summaries that are not factually consistent with the source document, severely …

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that
are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well …

The generalization problem on KBQA has drawn considerable attention. Existing research
suffers from the generalization issue brought by the entanglement in the coarse-grained …

Recent advances in deep learning have greatly propelled the research on semantic parsing.
Improvement has since been made in many downstream tasks, including natural language …

In collaborative learning environments, effective intelligent learning systems need to
accurately analyze and understand the collaborative discourse between learners (ie, group …

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained
sequence-to-sequence Transformer models has recently led to large improvements on AMR …