Semantic parsing aims at mapping natural language utterances into structured meaning
representations. In this work, we propose a structure-aware neural architecture which …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

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

Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural
models with no task-specific prior knowledge to achieve good results. In this paper, we …

Two important aspects of semantic parsing for question answering are the breadth of the
knowledge source and the depth of logical compositionality. While existing work trades off …

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on
annotated logical forms, which is especially expensive to obtain at large scale, we learn from …

We present an approach to rapidly and easily build natural language interfaces to
databases for new domains, whose performance improves over time based on user …

A code generation system generates programming language code based on an input
natural language description. State-of-the-art approaches rely on neural networks for code …

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 …

Computational semantics has long been considered a field divided between logical and
statistical approaches, but this divide is rapidly eroding with the development of statistical …