Conversational systems have come a long way since their inception in the 1960s. After
decades of research and development, we have seen progress from Eliza and Parry in the …

Intent classification, to identify the speaker's intention, and slot filling, to label each token
with a semantic type, are critical tasks in natural language understanding. Traditionally the …

Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

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 …

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 …

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-
SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 …

We present QuAC, a dataset for Question Answering in Context that contains 14K
information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd …

We present BRIDGE, a powerful sequential architecture for modeling dependencies
between natural language questions and relational databases in cross-DB semantic …

Spoken Language Understanding infers semantic meaning directly from audio data, and
thus promises to reduce error propagation and misunderstandings in end-user applications …

Synthesizing SQL queries from natural language is a long-standing open problem and has
been attracting considerable interest recently. Toward solving the problem, the de facto …