Towards a Dataset for Human Computer Communication via Grounded Language Acquisition

By Yonatan Bisk, Daniel Marcu, William Wong. 2016

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Citations in the corpus

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (2019, methodology): “e with sub-problems including visual semantic navigation, object detection, referring expression grounding, and action grounding. These challenges may be approachable by models that exploit hierarchy [26,8], modularity [4,16], and structured reasoning and planning [6]. Such approaches have not been ”
TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments (2018, background): “The larger environment requires following the instructions closely, as ﬁnding the goal using search strategies is unlikely, even given a large number of steps.”
Benchmarking Hierarchical Script Knowledge (2019, methodology): “2 Every instruction is paired with the original YouTube video and YouCook caption so the annotator could see how 1 Notable exceptions include the hierarchical instructions of (Regneri et al., 2013) and (Bisk et al., 2016).”
Learning Interpretable Spatial Operations in a Rich 3D Blocks World (2017, background): “Simultaneously, work within NLP (Bisk, Marcu, and Wong 2016; Wang, Liang, and Manning 2016) and Robotics (Li et al. 2016) returned to the question of action taking and scene understanding in SHRDLU style worlds.”
Natural Language Communication with Robots (2016, methodology): “In order to build models that understand the ambiguous and complex language used by people when communicating to solve a task, we adopt the Problem-Solution Sequence (PSS) framework proposed by Bisk et al. (2016).”
Cognitive Architectures for Language Agents (2023, background): “Classic linguistic interactions allow the agent to accept instructions (Winograd, 1972; Tellex et al., 2011; Chen and Mooney, 2011; Bisk et al., 2016) or learn from people (Nguyen et al., 2021; Sumers et al., 2022; 2021; Wang et al., 2016).”