17 research outputs found
Zero-shot Conversational Summarization Evaluations with small Large Language Models
Large Language Models (LLMs) exhibit powerful summarization abilities.
However, their capabilities on conversational summarization remains under
explored. In this work we evaluate LLMs (approx. 10 billion parameters) on
conversational summarization and showcase their performance on various prompts.
We show that the summaries generated by models depend on the instructions and
the performance of LLMs vary with different instructions sometimes resulting
steep drop in ROUGE scores if prompts are not selected carefully. We also
evaluate the models with human evaluations and discuss the limitations of the
models on conversational summarizationComment: Accepted at RoF0Mo workshop at Neurips 202
Distill and Collect for Semi-Supervised Temporal Action Segmentation
Recent temporal action segmentation approaches need frame annotations during
training to be effective. These annotations are very expensive and
time-consuming to obtain. This limits their performances when only limited
annotated data is available. In contrast, we can easily collect a large corpus
of in-domain unannotated videos by scavenging through the internet. Thus, this
paper proposes an approach for the temporal action segmentation task that can
simultaneously leverage knowledge from annotated and unannotated video
sequences. Our approach uses multi-stream distillation that repeatedly refines
and finally combines their frame predictions. Our model also predicts the
action order, which is later used as a temporal constraint while estimating
frames labels to counter the lack of supervision for unannotated videos. In the
end, our evaluation of the proposed approach on two different datasets
demonstrates its capability to achieve comparable performance to the full
supervision despite limited annotation
Real-Time Understanding of Complex Discriminative Scene Descriptions
Manuvinakurike R, Kennington C, DeVault D, Schlangen D. Real-Time Understanding of Complex Discriminative Scene Descriptions. In: Proceedings of the 17th Annual SIGdial Meeting on Discourse and Dialogue. 2016
Toward Incremental Dialogue Act Segmentation in Fast-Paced Interactive Dialogue Systems
Manuvinakurike R, Paetzel M, Qu C, Schlangen D, DeVault D. Toward Incremental Dialogue Act Segmentation in Fast-Paced Interactive Dialogue Systems. In: Proceedings of the 17th Annual SIGdial Meeting on Discourse and Dialogue. 2016
PentoRef: A Corpus of Spoken References in Task-oriented Dialogues
Zarrieß S, Hough J, Kennington C, et al. PentoRef: A Corpus of Spoken References in Task-oriented Dialogues. In: 10th edition of the Language Resources and Evaluation Conference. 2016
"Can you say more about the location?" : The Development of a Pedagogical Reference Resolution Agent
In an increasingly globalized world, geographic literacy is crucial. In this paper, we present a collaborative two-player game to improve people's ability to locate countries on the world map. We discuss two implementations of the game: First, we created a web-based version which can be played with the remote-controlled agent Nellie. With the knowledge we gained from a large online data collection, we re-implemented the game so it can be played face-to-face with the Furhat robot Neil. Our analysis shows that participants found the game not just engaging to play, they also believe they gained lasting knowledge about the world map
"Can you say more about the location?" : The Development of a Pedagogical Reference Resolution Agent
In an increasingly globalized world, geographic literacy is crucial. In this paper, we present a collaborative two-player game to improve people's ability to locate countries on the world map. We discuss two implementations of the game: First, we created a web-based version which can be played with the remote-controlled agent Nellie. With the knowledge we gained from a large online data collection, we re-implemented the game so it can be played face-to-face with the Furhat robot Neil. Our analysis shows that participants found the game not just engaging to play, they also believe they gained lasting knowledge about the world map
RDG-Map: A Multimodal Corpus of Pedagogical Human-Agent Spoken Interactions
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