The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …

With the power of parallel processing, large datasets, and fast computational resources,
deep neural networks (DNNs) have outperformed highly trained and experienced human …

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

The rise of sophisticated black-box machine learning models in Artificial Intelligence
systems has prompted the need for explanation methods that reveal how these models work …

The rise of large language models (LLMs) had a transformative impact on search, ushering
in a new era of search engines that are capable of generating search results in natural …

Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to
explore a topic. In such a conversational setting, the user's inputs are often incomplete, with …

Abstract We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing
framework for large scale knowledge base question answering (KBQA). ReTraCk is …

Multi-relation Question Answering is a challenging task, due to the requirement of
elaborated analysis on questions and reasoning over multiple fact triples in knowledge …

The rise of personal assistants has made conversational question answering (ConvQA) a
very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA …

In conversational question answering, users express their information needs through a
series of utterances with incomplete context. Typical ConvQA methods rely on a single …