Year: 2024
Type: conference-paper
Abstract: When engaging in collaborative tasks, humans efficiently exploit the semantic structure of a conversation to optimize verbal and nonverbal interactions.But in recent "language to code" or "language to action" models, this information is lacking.We show how incorporating the prior discourse and nonlinguistic context of a conversation situated in a nonlinguistic environment can improve the "language to action" component of such interactions.We finetune an LLM to predict actions based on prior context; our model, Nebula, doubles the net-action F1 score over the baseline on this task of Jayannavar et al. (2020).We also investigate our model's ability to construct shapes and understand location descriptions using a synthetic dataset.
Authors: Akshay Chaturvedi, Kate Thompson, Nicholas Asher
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404781498