Natural language interactions between humans and robots are intended to be situated in the sense that both user and robot can access and refer to the shared environment. Contextual knowledge plays a key role in resolving the ambiguities inherent in interpretation tasks. In addition, we expect the interpretation produced to be well-founded, e.g., that all mentions of entities in the environment (as perceived by the robot) are correctly grounded. In this paper, we propose the application of a transformer-based architecture that combines the input utterance with a linguistic description of the environment to produce interpretations and references to the environment in an end-to-end fashion. Experimental results demonstrate the robustness of the proposed methodology, overcoming previous approaches in which linguistic interpretation and grounding are composed of possible complex processing chains.

Hromei, C.d., Croce, D., Basili, R. (2022). Grounding end-to-end Architectures for Semantic Role Labeling in Human Robot Interaction. In NL4AI 2022: Sixth Workshop on Natural Language for Artificial Intelligence: proceedings of the Sixth Workshop on Natural Language for Artificial Intelligence (NL4AI 2022) co-located with 21th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2022) (pp.24-38). CEUR-WS.

Grounding end-to-end Architectures for Semantic Role Labeling in Human Robot Interaction

Hromei C. D.;Croce D.;Basili R.
2022-01-01

Abstract

Natural language interactions between humans and robots are intended to be situated in the sense that both user and robot can access and refer to the shared environment. Contextual knowledge plays a key role in resolving the ambiguities inherent in interpretation tasks. In addition, we expect the interpretation produced to be well-founded, e.g., that all mentions of entities in the environment (as perceived by the robot) are correctly grounded. In this paper, we propose the application of a transformer-based architecture that combines the input utterance with a linguistic description of the environment to produce interpretations and references to the environment in an end-to-end fashion. Experimental results demonstrate the robustness of the proposed methodology, overcoming previous approaches in which linguistic interpretation and grounding are composed of possible complex processing chains.
6th Workshop on Natural Language for Artificial Intelligence, NL4AI 2022
Udine, Italia
2022
6
Rilevanza internazionale
2022
Settore INF/01
Settore ING-INF/05
English
End to End Sequence to Sequence Architectures
Grounded Semantic Role Labeling
Human Robot Interaction
Perception
Robotics
Intervento a convegno
Hromei, C.d., Croce, D., Basili, R. (2022). Grounding end-to-end Architectures for Semantic Role Labeling in Human Robot Interaction. In NL4AI 2022: Sixth Workshop on Natural Language for Artificial Intelligence: proceedings of the Sixth Workshop on Natural Language for Artificial Intelligence (NL4AI 2022) co-located with 21th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2022) (pp.24-38). CEUR-WS.
Hromei, Cd; Croce, D; Basili, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/359274
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