Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126431" target="_blank" >RIV/00216224:14330/22:00126431 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-3180/paper-06.pdf" target="_blank" >http://ceur-ws.org/Vol-3180/paper-06.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)

Popis výsledku v původním jazyce

Sparse retrieval techniques can detect exact matches, but are inadequate for mathematical texts, where the same information can be expressed as either text or math. The soft vector space model has been shown to improve sparse retrieval on semantic text similarity, text classification, and machine translation evaluation tasks, but it has not yet been properly evaluated on math information retrieval. In our work, we compare the soft vector space model against standard sparse retrieval baselines and state-of-the-art math information retrieval systems from Task 1 (Answer Retrieval) of the ARQMath-3 lab. We evaluate the impact of different math representations, different notions of similarity between key words and math symbols ranging from Levenshtein distances to deep neural language models, and different ways of combining text and math. We show that using the soft vector space model consistently improves effectiveness compared to using standard sparse retrieval techniques. We also show that the Tangent-L math representation achieves better effectiveness than LaTeX, and that modeling text and math separately using two models improves effectiveness compared to jointly modeling text and math using a single model. Lastly, we show that different math representations and different ways of combining text and math benefit from different notions of similarity between tokens. Our best system achieves NDCG' of 0.251 on Task 1 of the ARQMath-3 lab.

Název v anglickém jazyce

Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)

Popis výsledku anglicky

Sparse retrieval techniques can detect exact matches, but are inadequate for mathematical texts, where the same information can be expressed as either text or math. The soft vector space model has been shown to improve sparse retrieval on semantic text similarity, text classification, and machine translation evaluation tasks, but it has not yet been properly evaluated on math information retrieval. In our work, we compare the soft vector space model against standard sparse retrieval baselines and state-of-the-art math information retrieval systems from Task 1 (Answer Retrieval) of the ARQMath-3 lab. We evaluate the impact of different math representations, different notions of similarity between key words and math symbols ranging from Levenshtein distances to deep neural language models, and different ways of combining text and math. We show that using the soft vector space model consistently improves effectiveness compared to using standard sparse retrieval techniques. We also show that the Tangent-L math representation achieves better effectiveness than LaTeX, and that modeling text and math separately using two models improves effectiveness compared to jointly modeling text and math using a single model. Lastly, we show that different math representations and different ways of combining text and math benefit from different notions of similarity between tokens. Our best system achieves NDCG' of 0.251 on Task 1 of the ARQMath-3 lab.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

15

Strana od-do

104-118

Název nakladatele

CEUR-WS

Místo vydání

Bologna

Místo konání akce

Bologna

Datum konání akce

5. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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