Draw Me a Flower: Processing and Grounding Abstraction in Natural Language

Royi Lachmy, Valentina Pyatkin, Avshalom Manevich, Reut Tsarfaty

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


ion 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 elic-itation method and present HEXAGONS, a2D instruction-following game. Using HEXAGONS we collected over 4k naturally occurring visually-grounded instructions rich with di-verse 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 in-ferior to human performance, and that model 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.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)1341-1356
عدد الصفحات16
دوريةTransactions of the Association for Computational Linguistics
مستوى الصوت10
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 28 نوفمبر 2022
منشور خارجيًانعم

ملاحظة ببليوغرافية

Publisher Copyright:
© 2022 Association for Computational Linguistics.


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