Task-Oriented Dialogue with In-Context Learning

1 code implementation • 19 Feb 2024 • Tom Bocklisch, Thomas Werkmeister, Daksh Varshneya, Alan Nichol

We describe a system for building task-oriented dialogue systems combining the in-context learning abilities of large language models (LLMs) with the deterministic execution of business logic.

In-Context Learning Navigate +1

DIET: Lightweight Language Understanding for Dialogue Systems

2 code implementations • 21 Apr 2020 • Tanja Bunk, Daksh Varshneya, Vladimir Vlasov, Alan Nichol

Large-scale pre-trained language models have shown impressive results on language understanding benchmarks like GLUE and SuperGLUE, improving considerably over other pre-training methods like distributed representations (GloVe) and purely supervised approaches.

Human Trajectory Prediction using Spatially aware Deep Attention Models

no code implementations • 26 May 2017 • Daksh Varshneya, G. Srinivasaraghavan

All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects.

Deep Attention Trajectory Prediction