Search Results for author:
Found 9 papers, 2 papers with code
Conversational Semantic Parsing for Dialog State Tracking
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models
Interestingly, the predictions by this model on images with no humans, are also visibly different from the one trained on gendered captions.
Fairness in AI Systems: Mitigating gender bias from language-vision models
Our society is plagued by several biases, including racial biases, caste biases, and gender bias.
Referring to Screen Texts with Voice Assistants
We collect a dataset and propose a lightweight general-purpose model for this novel experience.
Intelligent Assistant Language Understanding On Device
It has recently become feasible to run personal digital assistants on phones and other personal devices.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
