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 …

We present a new semantic parsing model for answering compositional questions on semi-
structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key …

We analyze state-of-the-art deep learning models for three tasks: question answering on (1)
images,(2) tables, and (3) passages of text. Using the notion of\emph {attribution}(word …

Complex question answering over knowledge base (Complex KBQA) is challenging
because it requires various compositional reasoning capabilities, such as multi-hop …

Our goal is to learn a semantic parser that maps natural language utterances into
executable programs when only indirect supervision is available: examples are labeled with …

A significant amount of information in today's world is stored in structured and semi-
structured knowledge bases. Efficient and simple methods to query them are essential and …

Abstract We present Memory Augmented Policy Optimization (MAPO), a simple and novel
way to leverage a memory buffer of promising trajectories to reduce the variance of policy …

We consider the problem of learning from sparse and underspecified rewards, where an
agent receives a complex input, such as a natural language instruction, and needs to …

We present a generative model to map natural language questions into SQL queries.
Existing neural network based approaches typically generate a SQL query word-by-word …

We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-
matrix reified KB. This representation enables neural modules that are fully differentiable …