Author name cluster

Natalie Parde

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2018 Conference Paper

Exploring the Terrain of Metaphor Novelty: A Regression-Based Approach for Automatically Scoring Metaphors

Natalie Parde
Rodney Nielsen

Automatically scoring metaphor novelty has been largely unexplored, but could be of beneﬁt to a wide variety of NLP applications. We introduce a large, publicly available metaphor novelty dataset to stimulate research in this area, and propose a regression-based approach to automatically score the novelty of potential metaphors that are expressed as word pairs. We additionally investigate which types of features are most useful for this task, and show that our approach outperforms baseline metaphor novelty scoring and standard metaphor detection approaches on this task.

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AAAI Conference 2018 Short Paper

Reading With Robots: Towards a Human-Robot Book Discussion System for Elderly Adults

Natalie Parde

As people age, it is critical that they maintain not only their physical health, but also their cognitive health—for instance, by engaging in cognitive exercise. Recent advancements in AI have uncovered novel ways through which to facilitate such exercise. In this thesis, I propose the ﬁrst human-robot dialogue system designed speciﬁcally to promote cognitive exercise in elderly adults, through discussions about interesting metaphors in books. I describe my work to date, including the development of a new, large corpus and an approach for automatically scoring metaphor novelty. Finally, I outline my plans for incorporating this work into the proposed system.

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IJCAI Conference 2015 Conference Paper

Grounding the Meaning of Words through Vision and Interactive Gameplay

Natalie Parde
Adam Hair
Michalis Papakostas
Konstantinos Tsiakas
Maria Dagioglou
Vangelis Karkaletsis
Rodney D. Nielsen

Currently, there exists a need for simple, easilyaccessible methods with which individuals lacking advanced technical training can expand and customize their robot’s knowledge. This work presents a means to satisfy that need, by abstracting the task of training robots to learn about the world around them as a vision- and dialogue-based game, I Spy. In our implementation of I Spy, robots gradually learn about objects and the concepts that describe those objects through repeated gameplay. We show that I Spy is an effective approach for teaching robots how to model new concepts using representations comprised of visual attributes. The results from 255 test games show that the system was able to correctly determine which object the human had in mind 67% of the time. Furthermore, a model evaluation showed that the system correctly understood the visual representations of its learned concepts with an average of 65% accuracy. Human accuracy against the same evaluation standard was just 88% on average.

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AAAI Conference 2015 Conference Paper

Is It Rectangular? Using I Spy as an Interactive, Game-Based Approach to Multimodal Robot Learning

Natalie Parde
Michalis Papakostas
Konstantinos Tsiakas
Rodney Nielsen

Training robots about the objects in their environment requires a multimodal correlation of features extracted from visual and linguistic sources. This work abstracts the task of collecting multimodal training data for object and feature learning by encapsulating it in an interactive game, I Spy, played between human players and robots. It introduces the concept of the game, briefly describes its methodology, and finally presents an evaluation of the game’s performance and its appeal to human players.

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