15 citations · 34 across the 11 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…
A Complex KBQA System using Multiple Reasoning Paths
Kechen Qin, Yu Wang, Cheng Li +4
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them…
Neural Entity Summarization with Joint Encoding and Weak Supervision
Junyou Li, Gong Cheng, Qingxia Liu +4
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions…
ESBM: An Entity Summarization BenchMark
Qingxia Liu, Gong Cheng, Kalpa Gunaratna +1
Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supp…
Entity Summarization: State of the Art and Future Challenges
Qingxia Liu, Gong Cheng, Kalpa Gunaratna +1
The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. T…
Document Retrieval using Predication Similarity
Kalpa Gunaratna
Document retrieval has been an important research problem over many years in the information retrieval community. State-of-the-art techniques utilize various methods in matching do…