Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

In the NLP community, recent years have seen a surge of research activities that address
machines' ability to perform deep language understanding which goes beyond what is …

To bridge the gap between users and data, numerous text-to-SQL systems have been
developed that allow users to pose natural language questions over relational databases …

Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of providing a
system with explicit, general knowledge and having the system reason over that knowledge …

Transformers have been shown to emulate logical deduction over natural language theories
(logical rules expressed in natural language), reliably assigning true/false labels to …

Large pre-trained language models (LMs) are known to encode substantial amounts of
linguistic information. However, high-level reasoning skills, such as numerical reasoning …

Relational structures such as schema linking and schema encoding have been validated as
a key component to qualitatively translating natural language into SQL queries. However …

Most recently, there has been significant interest in learning contextual representations for
various NLP tasks, by leveraging large scale text corpora to train powerful language models …

Semantic parsing is a technique aimed at constructing a structured representation of the
meaning of a natural-language question. Recent advancements in few-shot language …