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Shashi Bhushan TN
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2020 – today
- 2024
- [i10]Xue-Yong Fu, Md. Tahmid Rahman Laskar, Elena Khasanova, Cheng Chen, Shashi Bhushan TN:
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization? CoRR abs/2402.00841 (2024) - [i9]Md. Tahmid Rahman Laskar, Elena Khasanova, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN:
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization. CoRR abs/2403.00067 (2024) - 2023
- [c8]Md. Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Mahsa Azizi, Shashi Bhushan TN, Simon Corston-Oliver:
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching. ACL (industry) 2023: 599-607 - [c7]Md. Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN:
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective. EMNLP (Industry Track) 2023: 343-352 - [i8]Md. Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Mahsa Azizi, Shashi Bhushan TN, Simon Corston-Oliver:
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching. CoRR abs/2305.17619 (2023) - [i7]Md. Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN:
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective. CoRR abs/2310.19233 (2023) - [i6]Xue-Yong Fu, Md. Tahmid Rahman Laskar, Cheng Chen, Shashi Bhushan TN:
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs. CoRR abs/2311.00681 (2023) - 2022
- [c6]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver:
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts. W-NUT@COLING 2022: 96-100 - [c5]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, Shashi Bhushan TN:
Entity-level Sentiment Analysis in Contact Center Telephone Conversations. EMNLP (Industry Track) 2022: 484-491 - [c4]Md. Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan TN, Simon Corston-Oliver:
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations. NAACL-HLT (Industry Papers) 2022: 344-352 - [c3]Md. Tahmid Rahman Laskar, Cheng Chen, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan TN, Simon Corston-Oliver:
An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations. SIGIR 2022: 3363-3367 - [i5]Md. Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan TN, Simon Corston-Oliver:
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations. CoRR abs/2205.04438 (2022) - [i4]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver:
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts. CoRR abs/2209.13736 (2022) - [i3]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, Shashi Bhushan TN:
Entity-level Sentiment Analysis in Contact Center Telephone Conversations. CoRR abs/2210.13401 (2022) - [i2]Md. Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Shashi Bhushan TN:
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop. CoRR abs/2211.01354 (2022) - 2021
- [c2]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver:
Improving Punctuation Restoration for Speech Transcripts via External Data. W-NUT 2021: 168-174 - [i1]Xue-Yong Fu, Cheng Chen, Md. Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver:
Improving Punctuation Restoration for Speech Transcripts via External Data. CoRR abs/2110.00560 (2021)
2010 – 2019
- 2018
- [c1]Simon Caton
, Srikumar Venugopal, Shashi Bhushan TN, Vidya Sankar Velamuri, Kostas Katrinis:
Dynamic Model Evaluation to Accelerate Distributed Machine Learning. BigData Congress 2018: 150-157
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