In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities.
The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets.
To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset.
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users.
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL).