200 citations · 233 across the 6 of their papers we have counts for
9 papers · 1 filter
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar +2
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits o…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…
ProFormer: Towards On-Device LSH Projection Based Transformers
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints…
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar +7
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementa…
On-Device Text Representations Robust To Misspellings via Projections
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to pr…
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal +2
Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily adapted to new domains, and require minima…