Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.

Recent work on Natural Language Interfaces to Databases (NLIDB) has attracted considerable attention. NLIDB allow users to search databases using natural language instead of SQL-like query languages. While saving the users from having to learn query languages, multi-turn interaction with NLIDB usually involves multiple queries where contextual information is vital to understand the users' query intents. In this paper, we address a typical contextual understanding problem, termed as follow-up query analysis. In spite of its ubiquity, follow-up query analysis has not been well studied due to two primary obstacles: the multifarious nature of follow-up query scenarios and the lack of high-quality datasets. Our work summarizes typical follow-up query scenarios and provides a new FollowUp dataset with $1000$ query triples on 120 tables. Moreover, we propose a novel approach FANDA, which takes into account the structures of queries and employs a ranking model with weakly supervised max-margin learning. The experimental results on FollowUp demonstrate the superiority of FANDA over multiple baselines across multiple metrics.

Tucked within Google's unremarkable Allo messaging app is a real treasure: Google Assistant, which injects Google Now with an eager-to-please personality that finally provides the give-and-take other digital assistants lack. We've always talked about Apple's Siri, Microsoft's Cortana, and Google Now as the three digital assistants from the top smartphone platforms. But the truth is that Google Now was little more than a series of informative cards, while Siri and Cortana preferred a text-based approach with a bit of sass. Google Assistant retains its visual approach, but within a messaging context that really nails it in how you interact with the app itself. Google announced Google Assistant this past May, and the preview version of it is live in Allo, which itself can be used on Android 4.1 (Jelly Bean) on up.