Draw Me a Flower: Processing and Grounding Abstraction in Natural Language
This work addresses a foundational gap in NLP for processing abstraction in natural language, which is incremental as it sets the stage for future research but does not solve the problem.
The paper tackled the problem of interpreting and grounding abstraction in natural language instructions, which lacks systematic study and benchmarks in NLP, by introducing Hexagons, a 2D instruction-following game that collected over 4k instructions with diverse abstractions. The results showed that contemporary neural models are substantially inferior to human performance, with performance inversely correlated to abstraction levels, confirming abstraction as a challenging phenomenon in NLP/AI.
Abstraction is a core tenet of human cognition and communication. When composing natural language instructions, humans naturally evoke abstraction to convey complex procedures in an efficient and concise way. Yet, interpreting and grounding abstraction expressed in NL has not yet been systematically studied in NLP, with no accepted benchmarks specifically eliciting abstraction in NL. In this work, we set the foundation for a systematic study of processing and grounding abstraction in NLP. First, we deliver a novel abstraction elicitation method and present Hexagons, a 2D instruction-following game. Using Hexagons we collected over 4k naturally-occurring visually-grounded instructions rich with diverse types of abstractions. From these data, we derive an instruction-to-execution task and assess different types of neural models. Our results show that contemporary models and modeling practices are substantially inferior to human performance, and that models' performance is inversely correlated with the level of abstraction, showing less satisfying performance on higher levels of abstraction. These findings are consistent across models and setups, confirming that abstraction is a challenging phenomenon deserving further attention and study in NLP/AI research.