Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

Abstract The Visual Question Answering (VQA) task aspires to provide a meaningful testbed
for the development of AI models that can jointly reason over visual and natural language …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …

Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …

When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …