15 citations · 30 across the 8 of their papers we have counts for
11 papers
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this ar…
AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan +8
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT data…
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola +1
Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features…
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs
Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan +1
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and sati…
Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones
Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama +1
Information Extraction from visual documents enables convenient and intelligent assistance to end users. We present a Neighborhood-based Information Extraction (NIE) approach that…
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web
Kalpa Gunaratna, Yu Wang, Hongxia Jin
Knowledge is captured in the form of entities and their relationships and stored in knowledge graphs. Knowledge graphs enhance the capabilities of applications in many different ar…