Search Results for author: Kalpa Gunaratna

Found 11 papers, 4 papers with code

Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond

no code implementations25 Sep 2023 Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin

Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy.

General Classification Intent Detection +6

AlpaGasus: Training A Better Alpaca with Fewer Data

3 code implementations17 Jul 2023 Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin

Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.

Instruction Following

Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling

no code implementations19 Oct 2022 Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola, Hongxia Jin

In this work, we propose a novel approach that: (i) learns to generate additional slot type specific features in order to improve accuracy and (ii) provides explanations for slot filling decisions for the first time in a joint NLU model.

Intent Detection Natural Language Understanding +2

ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

1 code implementation13 Dec 2021 Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin

To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query.

Information Retrieval Knowledge Graphs +3

Neural Entity Summarization with Joint Encoding and Weak Supervision

1 code implementation1 May 2020 Junyou Li, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, Huajun Chen

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.

ESBM: An Entity Summarization BenchMark

no code implementations8 Mar 2020 Qingxia Liu, Gong Cheng, Kalpa Gunaratna, Yuzhong Qu

In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark.

Entity Summarization: State of the Art and Future Challenges

no code implementations18 Oct 2019 Qingxia Liu, Gong Cheng, Kalpa Gunaratna, Yuzhong Qu

This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively.

Combinatorial Optimization Information Retrieval +2

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