<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fu-etal-2021-improving">
    <titleInfo>
        <title>Improving Punctuation Restoration for Speech Transcripts via External Data</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xue-Yong</namePart>
        <namePart type="family">Fu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Cheng</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Md</namePart>
        <namePart type="given">Tahmid</namePart>
        <namePart type="given">Rahman</namePart>
        <namePart type="family">Laskar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shashi</namePart>
        <namePart type="family">Bhushan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Simon</namePart>
        <namePart type="family">Corston-Oliver</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tim</namePart>
            <namePart type="family">Baldwin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Afshin</namePart>
            <namePart type="family">Rahimi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>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, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1.12% F1 score.</abstract>
    <identifier type="citekey">fu-etal-2021-improving</identifier>
    <identifier type="doi">10.18653/v1/2021.wnut-1.19</identifier>
    <location>
        <url>https://aclanthology.org/2021.wnut-1.19</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>168</start>
            <end>174</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Punctuation Restoration for Speech Transcripts via External Data
%A Fu, Xue-Yong
%A Chen, Cheng
%A Laskar, Md Tahmid Rahman
%A Bhushan, Shashi
%A Corston-Oliver, Simon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F fu-etal-2021-improving
%X 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, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1.12% F1 score.
%R 10.18653/v1/2021.wnut-1.19
%U https://aclanthology.org/2021.wnut-1.19
%U https://doi.org/10.18653/v1/2021.wnut-1.19
%P 168-174

Markdown (Informal)

[Improving Punctuation Restoration for Speech Transcripts via External Data](https://aclanthology.org/2021.wnut-1.19) (Fu et al., WNUT 2021)

• Improving Punctuation Restoration for Speech Transcripts via External Data (Fu et al., WNUT 2021)

ACL

• Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan, and Simon Corston-Oliver. 2021. Improving Punctuation Restoration for Speech Transcripts via External Data. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 168–174, Online. Association for Computational Linguistics.