<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="benamar-etal-2022-evaluating">
    <titleInfo>
        <title>Evaluating Tokenizers Impact on OOVs Representation with Transformers Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Benamar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Cyril</namePart>
        <namePart type="family">Grouin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Meryl</namePart>
        <namePart type="family">Bothua</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anne</namePart>
        <namePart type="family">Vilnat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Transformer models have achieved significant improvements in multiple downstream tasks in recent years. One of the main contributions of Transformers is their ability to create new representations for out-of-vocabulary (OOV) words. In this paper, we have evaluated three categories of OOVs: (A) new domain-specific terms (e.g., “eucaryote”’ in microbiology), (B) misspelled words containing typos, and (C) cross-domain homographs (e.g., “arm” has different meanings in a clinical trial and anatomy). We use three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. Our experiments have led to exciting findings that showed: (1) It is easier to improve the representation of new words (A and B) than it is for words that already exist in the vocabulary of the Transformer models (C), (2) To ameliorate the representation of OOVs, the most effective method relies on adding external morpho-syntactic context rather than improving the semantic understanding of the words directly (fine-tuning) and (3) We cannot foresee the impact of minor misspellings in words because similar misspellings have different impacts on their representation. We believe that tackling the challenges of processing OOVs regarding their specificities will significantly help the domain adaptation aspect of BERT.</abstract>
    <identifier type="citekey">benamar-etal-2022-evaluating</identifier>
    <location>
        <url>https://aclanthology.org/2022.lrec-1.445</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>4193</start>
            <end>4204</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Evaluating Tokenizers Impact on OOVs Representation with Transformers Models
%A Benamar, Alexandra
%A Grouin, Cyril
%A Bothua, Meryl
%A Vilnat, Anne
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F benamar-etal-2022-evaluating
%X Transformer models have achieved significant improvements in multiple downstream tasks in recent years. One of the main contributions of Transformers is their ability to create new representations for out-of-vocabulary (OOV) words. In this paper, we have evaluated three categories of OOVs: (A) new domain-specific terms (e.g., “eucaryote”’ in microbiology), (B) misspelled words containing typos, and (C) cross-domain homographs (e.g., “arm” has different meanings in a clinical trial and anatomy). We use three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. Our experiments have led to exciting findings that showed: (1) It is easier to improve the representation of new words (A and B) than it is for words that already exist in the vocabulary of the Transformer models (C), (2) To ameliorate the representation of OOVs, the most effective method relies on adding external morpho-syntactic context rather than improving the semantic understanding of the words directly (fine-tuning) and (3) We cannot foresee the impact of minor misspellings in words because similar misspellings have different impacts on their representation. We believe that tackling the challenges of processing OOVs regarding their specificities will significantly help the domain adaptation aspect of BERT.
%U https://aclanthology.org/2022.lrec-1.445
%P 4193-4204

Markdown (Informal)

[Evaluating Tokenizers Impact on OOVs Representation with Transformers Models](https://aclanthology.org/2022.lrec-1.445) (Benamar et al., LREC 2022)

• Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (Benamar et al., LREC 2022)

ACL

• Alexandra Benamar, Cyril Grouin, Meryl Bothua, and Anne Vilnat. 2022. Evaluating Tokenizers Impact on OOVs Representation with Transformers Models. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4193–4204, Marseille, France. European Language Resources Association.