Abstract
Natural Language interactions between humans and robots are meant to be situated, in the sense that both the user and the robot can access and make reference to the shared environment. Contextual knowledge plays thus a key role in the solution of inherent ambiguities in interpretation tasks, such as Grounded Semantic Role Labeling (GSRL). Explicit representations for the context (i.e. the map description of the surroundings) are crucial and the possibility of injecting such information in the training stages of semantic interpreters is very appealing. In this paper, we propose to make a sequence-to-sequence model for GSRL, thus eliminating the traditional cascade of tasks and effectively linking real-world entities with their identifiers, that is sensitive to map information in form of linguistic descriptions. The corresponding generation process, based on BART, achieves results competitive with the state-of-the-art on the GSRL task.
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The code is available at https://github.com/crux82/grut.
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All the constraints are appended to the input, each of which is divided by a “#" delimiter character.
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The frames connected with a lexical unit, such as take, are only the ones in FrameNet that are needed in the domain knowledge base: TAKING and BRINGING are the only frames for take that are possible, i.e. defined, in the Huric dataset, dealing with the robotic command language.
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Acknowledgement
We would like to thank the “Istituto di Analisi dei Sistemi ed Informatica - Antonio Ruberti" (IASI) for supporting the experimentations through access to dedicated computing resources. Claudiu Daniel Hromei is a Ph.D. student enrolled in the National Ph.D. in Artificial Intelligence, XXXVII cycle, course on Health and life sciences, organized by the Università Campus Bio-Medico di Roma.
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Hromei, C.D., Cristofori, L., Croce, D., Basili, R. (2023). Embedding Contextual Information in Seq2seq Models for Grounded Semantic Role Labeling. In: Dovier, A., Montanari, A., Orlandini, A. (eds) AIxIA 2022 – Advances in Artificial Intelligence. AIxIA 2022. Lecture Notes in Computer Science(), vol 13796. Springer, Cham. https://doi.org/10.1007/978-3-031-27181-6_33
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