Controlling the text generated by language models and customizing the content has been a
long-standing challenge. Existing prompting techniques proposed in pursuit of providing …

Semantic parsing maps natural language questions into logical forms, which can be
executed against a knowledge base for answers. In real-world applications, the performance …

We explore the use of large language models (LLMs) for zero-shot semantic parsing.
Semantic parsing involves mapping natural language utterances to task-specific meaning …

Many discriminative natural language understanding (NLU) tasks have large label spaces.
Learning such a process of large-space decision making is particularly challenging due to …

Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands
expressed in natural language, transforming them into structured outputs that combine …

Automatic fact verification has received significant attention recently. Contemporary
automatic fact-checking systems focus on estimating truthfulness using numerical scores …

We have investigated methods utilizing hierarchical structure information representation in
the semantic parsing task and have devised a method that reinforces the semantic …

Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google
Assistant by mapping natural language to structured meaning representations. When we …

Voice assistants such as Amazon Alexa, Apple Siri, and Google Assistant have become
ubiquitous. They rely on spoken language understanding, which typically consists of an …

Large Language Models (LLMs) have spurred progress in text-to-SQL, the task of
generating SQL queries from natural language questions based on a given database …