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Full-Text Articles in Computational Linguistics
Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher
Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher
Articles
Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with …
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Articles
Errors in visual perception may cause problems in situated dialogues. We investigated this problem through an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot.We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions.We then introduced different methods for the user to request information about the robot’s understanding of the environment. We quantify the impact of perception errors on the dialogues, and investigate resolution attempts by users at a structural level and at the level of referring expressions.
Perception Based Misunderstandings In Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Perception Based Misunderstandings In Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Articles
In a situated dialogue, misunderstandings may arise if the participants perceive or interpret the environment in different ways. In human-computer dialogue this may be due the sensor errors. We present an experiment system and a series of experiments in which we investigate this problem.
Applying Computational Models Of Spatial Prepositions To Visually Situated Dialog, John D. Kelleher, Fintan Costello
Applying Computational Models Of Spatial Prepositions To Visually Situated Dialog, John D. Kelleher, Fintan Costello
Articles
This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact …