Výsledky vyhledávání - "Pang, Yutong"

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LLaMA based Punctuation Restoration With Forward Pass Only Decoding

Autor: Pang, Yutong, Paul, Debjyoti, Jiang, Kevin, Zhang, Xuedong, Lei, Xin

This paper introduces two advancements in the field of Large Language Model Annotation with a focus on punctuation restoration tasks. Our first contribution is the application of LLaMA for punctuation restoration, which demonstrates superior performa

Externí odkaz: http://arxiv.org/abs/2408.11845

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Recovering from Privacy-Preserving Masking with Large Language Models

Autor: Vats, Arpita, Liu, Zhe, Su, Peng, Paul, Debjyoti, Ma, Yingyi, Pang, Yutong, Ahmed, Zeeshan, Kalinli, Ozlem

Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural

Externí odkaz: http://arxiv.org/abs/2309.08628

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Contextual Biasing of Named-Entities with Large Language Models

Autor: Sun, Chuanneng, Ahmed, Zeeshan, Ma, Yingyi, Liu, Zhe, Kabela, Lucas, Pang, Yutong, Kalinli, Ozlem

This paper studies contextual biasing with Large Language Models (LLMs), where during second-pass rescoring additional contextual information is provided to a LLM to boost Automatic Speech Recognition (ASR) performance. We propose to leverage prompts

Externí odkaz: http://arxiv.org/abs/2309.00723

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Language Agnostic Data-Driven Inverse Text Normalization

Autor: Chen, Szu-Jui, Paul, Debjyoti, Pang, Yutong, Su, Peng, Zhang, Xuedong

With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need. This inverse text normalization (ITN) problem attracts the attention of researchers from various fields

Externí odkaz: http://arxiv.org/abs/2301.08506

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Improving Data Driven Inverse Text Normalization using Data Augmentation

Autor: Pandey, Laxmi, Paul, Debjyoti, Chitkara, Pooja, Pang, Yutong, Zhang, Xuedong, Schubert, Kjell, Chou, Mark, Liu, Shu, Saraf, Yatharth

Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. Traditional handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile neural modeling appro

Externí odkaz: http://arxiv.org/abs/2207.09674

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Akademický článek

Mixture-proportioning design of cement soil containing Pisha sandstone for mine filling

Autor: Jing, Yu, Liu, Xin, Qiao, Zheng, Liu, Zongxu, Pang, Yutong, Qi, Huiru, Wang, Junjie

Publikováno v: In Case Studies in Construction Materials July 2024 20

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Improving N-gram Language Models with Pre-trained Deep Transformer

Autor: Wang, Yiren, Huang, Hongzhao, Liu, Zhe, Pang, Yutong, Wang, Yongqiang, Zhai, ChengXiang, Peng, Fuchun

Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference. In this paper, we demonstrate that n-gram LM can be improved by

Externí odkaz: http://arxiv.org/abs/1911.10235

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