Search Results for author:
Found 9 papers, 0 papers with code
Towards Code-Mixed Hinglish Dialogue Generation
Code-mixed language plays a crucial role in communication in multilingual societies.
Hinglish to English Machine Translation using Multilingual Transformers
Code-Mixed language plays a very important role in communication in multilingual societies and with the recent increase in internet users especially in multilingual societies, the usage of such mixed language has also increased.
What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection
This inquiry drives us to algorithmically harness motion cues for identifying generic event boundaries in videos.
Ranked #1 on
Generic Event Boundary Detection
on TAPOS
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
Data-to-text (D2T) generation is a crucial task in many natural language understanding (NLU) applications and forms the foundation of task-oriented dialog systems.
Ranked #3 on
Data-to-Text Generation
on E2E NLG Challenge
PrivPAS: A real time Privacy-Preserving AI System and applied ethics
This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content.
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network
To build high-quality real-world conversational solutions for edge devices, there is a need for deploying intent detection model on device.
Ranked #1 on
Intent Detection
on SNIPS
(model size metric)
FONTNET: On-Device Font Understanding and Prediction Pipeline
In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font.
Ranked #5 on
Font Recognition
on AdobeVFR syn
EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts
To the best of our knowledge, this work presents the first lightweight deep learning approach for smartphone deployment of emphasis selection.
LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning
The proposed LiteMuL not only outperforms the current state of the art results but also surpasses the results of our proposed on-device task-specific models, with accuracy gains of up to 11% and model-size reduction by 50%-56%.
