Deepthi Karkada


Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task
Deepthi Karkada | Ramesh Manuvinakurike | Maike Paetzel-Prüsmann | Kallirroi Georgila
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a “strategy-level” without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people’s descriptive strategy to ease the system’s language understanding in creative tasks.


Nontrivial Lexical Convergence in a Geography-Themed Game
Amanda Bergqvist | Ramesh Manuvinakurike | Deepthi Karkada | Maike Paetzel
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

The present study aims to examine the prevalent notion that people entrain to the vocabulary of a dialogue system. Although previous research shows that people will replace their choice of words with simple substitutes, studies using more challenging substitutions are sparse. In this paper, we investigate whether people adapt their speech to the vocabulary of a dialogue system when the system’s suggested words are not direct synonyms. 32 participants played a geography-themed game with a remote-controlled agent and were primed by referencing strategies (rather than individual terms) introduced in follow-up questions. Our results suggest that context-appropriate substitutes support convergence and that the convergence has a lasting effect within a dialogue session if the system’s wording is more consistent with the norms of the domain than the original wording of the speaker.

RDG-Map: A Multimodal Corpus of Pedagogical Human-Agent Spoken Interactions.
Maike Paetzel | Deepthi Karkada | Ramesh Manuvinakurike
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper presents a multimodal corpus of 209 spoken game dialogues between a human and a remote-controlled artificial agent. The interactions involve people collaborating with the agent to identify countries on the world map as quickly as possible, which allows studying rapid and spontaneous dialogue with complex anaphoras, disfluent utterances and incorrect descriptions. The corpus consists of two parts: 8 hours of game interactions have been collected with a virtual unembodied agent online and 26.8 hours have been recorded with a physically embodied robot in a research lab. In addition to spoken audio recordings available for both parts, camera recordings and skeleton-, facial expression- and eye-gaze tracking data have been collected for the lab-based part of the corpus. In this paper, we introduce the pedagogical reference resolution game (RDG-Map) and the characteristics of the corpus collected. We also present an annotation scheme we developed in order to study the dialogue strategies utilized by the players. Based on a subset of 330 minutes of interactions annotated so far, we discuss initial insights into these strategies as well as the potential of the corpus for future research.


Towards Understanding End-of-trip Instructions in a Taxi Ride Scenario
Deepthi Karkada | Ramesh Manuvirakurike | Kallirroi Georgila
Proceedings 14th Joint ACL - ISO Workshop on Interoperable Semantic Annotation