no code implementations • 25 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.
3 code implementations • 17 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.
no code implementations • 19 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.
1 code implementation • 13 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.
no code implementations • 23 Aug 2021 • Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama, Hongxia Jin
Information Extraction from visual documents enables convenient and intelligent assistance to end users.
no code implementations • 29 Mar 2021 • Kalpa Gunaratna, Yu Wang, Hongxia Jin
Then we learn entity embeddings through this new type of triples.
no code implementations • 22 May 2020 • Kechen Qin, Yu Wang, Cheng Li, Kalpa Gunaratna, Hongxia Jin, Virgil Pavlu, Javed A. Aslam
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding.
1 code implementation • 1 May 2020 • Shuxin Li, Zixian Huang, Gong Cheng, Evgeny Kharlamov, Kalpa Gunaratna
A prominent application of knowledge graph (KG) is document enrichment.
1 code implementation • 1 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.
no code implementations • 8 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.
no code implementations • 18 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.