Making Sentence Embeddings Robust to User-Generated Content

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AIM6AH3UP" target="_blank" >RIV/00216208:11320/25:IM6AH3UP - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195981833&partnerID=40&md5=aeb5ae2ebb6451793d1ee0283fc11760" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195981833&partnerID=40&md5=aeb5ae2ebb6451793d1ee0283fc11760</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Making Sentence Embeddings Robust to User-Generated Content

Popis výsledku v původním jazyce

NLP models have been known to perform poorly on user-generated content (UGC), mainly because it presents a lot of lexical variations and deviates from the standard texts on which most of these models were trained. In this work, we focus on the robustness of LASER, a sentence embedding model, to UGC data. We evaluate this robustness by LASER's ability to represent non-standard sentences and their standard counterparts close to each other in the embedding space. Inspired by previous works extending LASER to other languages and modalities, we propose RoLASER, a robust English encoder trained using a teacher-student approach to reduce the distances between the representations of standard and UGC sentences. We show that with training only on standard and synthetic UGC-like data, RoLASER significantly improves LASER's robustness to both natural and artificial UGC data by achieving up to 2× and 11× better scores. We also perform a fine-grained analysis on artificial UGC data and find that our model greatly outperforms LASER on its most challenging UGC phenomena such as keyboard typos and social media abbreviations. Evaluation on downstream tasks shows that RoLASER performs comparably to or better than LASER on standard data, while consistently outperforming it on UGC data. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Název v anglickém jazyce

Making Sentence Embeddings Robust to User-Generated Content

Popis výsledku anglicky

NLP models have been known to perform poorly on user-generated content (UGC), mainly because it presents a lot of lexical variations and deviates from the standard texts on which most of these models were trained. In this work, we focus on the robustness of LASER, a sentence embedding model, to UGC data. We evaluate this robustness by LASER's ability to represent non-standard sentences and their standard counterparts close to each other in the embedding space. Inspired by previous works extending LASER to other languages and modalities, we propose RoLASER, a robust English encoder trained using a teacher-student approach to reduce the distances between the representations of standard and UGC sentences. We show that with training only on standard and synthetic UGC-like data, RoLASER significantly improves LASER's robustness to both natural and artificial UGC data by achieving up to 2× and 11× better scores. We also perform a fine-grained analysis on artificial UGC data and find that our model greatly outperforms LASER on its most challenging UGC phenomena such as keyboard typos and social media abbreviations. Evaluation on downstream tasks shows that RoLASER performs comparably to or better than LASER on standard data, while consistently outperforming it on UGC data. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Počet stran výsledku

15

Strana od-do

10984-10998

Název nakladatele

European Language Resources Association (ELRA)

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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