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Found 11 papers, 1 papers with code
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru
We develop a custom, state-of-the-art emotion recognition model to dynamically select the robot's tone of voice and utilize emojis from LLM output as cues for generating robot actions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Developing Autonomous Robot-Mediated Behavior Coaching Sessions with Haru
This study presents an empirical investigation into the design and impact of autonomous dialogues in human-robot interaction for behavior change coaching.
A Study on Social Robot Behavior in Group Conversation
Recently, research in human-robot interaction began to consider a robot's influence at the group level.
Collaborative Storytelling with Large-scale Neural Language Models
We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far.
Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix
State of the art sound event retrieval models have focused on single-label audio recordings, with only one sound event occurring, rather than on multi-label audio recordings (i. e., multiple sound events occur in one recording).
Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
Lexical Acquisition through Implicit Confirmations over Multiple Dialogues
We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues.
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets
In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task {\#}2: Named Entity Recognition in Twitter.
Named Entity Recognition with Bidirectional LSTM-CNNs
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Ranked #27 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
