Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language

• URL: http://arxiv.org/abs/2106.14321v1
• Date: Sun, 27 Jun 2021 21:11:16 GMT
• Title: Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language
• Authors: Royi Lachmy, Valentina Pyatkin, Reut Tsarfaty
• Abstract summary: We develop the Hexagons referential game, where players describe increasingly complex images on a two-dimensional Hexagons board. Using this game we collected the Hexagons dataset, which consists of 164 images and over 3000 naturally-occurring instructions. Results of our baseline models on an instruction-to-execution task derived from the Hexagons dataset confirm that higher-level abstractions in NL are indeed more challenging for current systems to process.
• Score: 6.900102922776184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally evoke abstract constructs such as objects, loops, conditions and functions to convey their intentions in an efficient and precise way. Yet, interpreting and grounding abstraction stated in NL has not been systematically studied in NLP/AI. To elicit naturally-occurring abstractions in NL we develop the Hexagons referential game, where players describe increasingly complex images on a two-dimensional Hexagons board, and other players need to follow these instructions to recreate the images. Using this game we collected the Hexagons dataset, which consists of 164 images and over 3000 naturally-occurring instructions, rich with diverse abstractions. Results of our baseline models on an instruction-to-execution task derived from the Hexagons dataset confirm that higher-level abstractions in NL are indeed more challenging for current systems to process. Thus, this dataset exposes a new and challenging dimension for grounded semantic parsing, and we propose it for the community as a future benchmark to explore more sophisticated and high-level communication within NLP applications.

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