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pro vyhledávání: '"TN, Shashi Bhushan"'
This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query. When using Large Language Models (LLMs) for this task, usually a new call to the L
Externí odkaz:
http://arxiv.org/abs/2403.00067
Large Language Models (LLMs) have demonstrated impressive capabilities to solve a wide range of tasks without being explicitly fine-tuned on task-specific datasets. However, deploying LLMs in the real world is not trivial, as it requires substantial
Externí odkaz:
http://arxiv.org/abs/2402.00841
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs
In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as evaluators for te
Externí odkaz:
http://arxiv.org/abs/2311.00681
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, name
Externí odkaz:
http://arxiv.org/abs/2310.19233
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but also such data may have lots of annotation errors even after the annota
Externí odkaz:
http://arxiv.org/abs/2211.01354
Autor:
Fu, Xue-Yong, Chen, Cheng, Laskar, Md Tahmid Rahman, Gardiner, Shayna, Hiranandani, Pooja, TN, Shashi Bhushan
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrat
Externí odkaz:
http://arxiv.org/abs/2210.13401
Autor:
Fu, Xue-Yong, Chen, Cheng, Laskar, Md Tahmid Rahman, TN, Shashi Bhushan, Corston-Oliver, Simon
We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recogn
Externí odkaz:
http://arxiv.org/abs/2209.13736
Autor:
Laskar, Md Tahmid Rahman, Chen, Cheng, Martsinovich, Aliaksandr, Johnston, Jonathan, Fu, Xue-Yong, TN, Shashi Bhushan, Corston-Oliver, Simon
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in production environments is a challen
Externí odkaz:
http://arxiv.org/abs/2205.04438
Autor:
Fu, Xue-Yong, Chen, Cheng, Laskar, Md Tahmid Rahman, TN, Shashi Bhushan, Corston-Oliver, Simon
Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper
Externí odkaz:
http://arxiv.org/abs/2110.00560