In this paper, we provide an overview of the existing methods for integrating human advice
into a Reinforcement Learning process. We first propose a taxonomy of the different forms of …

Question answering has emerged as an intuitive way of querying structured data sources
and has attracted significant advancements over the years. A large body of recent work on …

Semantic parsing aims at mapping natural language to machine interpretable meaning
representations. Traditional approaches rely on high-quality lexicons, manually-built …

This paper presents a new technique for automatically synthesizing SQL queries from
natural language (NL). At the core of our technique is a new NL-based program synthesis …

Answering natural language questions using the Freebase knowledge base has recently
been explored as a platform for advancing the state of the art in open domain semantic …

The context in which language is used provides a strong signal for learning to recover its
meaning. In this paper, we show it can be used within a grounded CCG semantic parsing …

Answering questions with data is a difficult and time-consuming process. Visual dashboards
and templates make it easy to get started, but asking more sophisticated questions often …

Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical forxm and computing the answer given a structured …

The ability to understand natural-language instructions is critical to building intelligent
agents that interact with humans. We present a system that learns to transform natural …

We consider the challenge of learning semantic parsers that scale to large, open-domain
problems, such as question answering with Freebase. In such settings, the sentences cover …