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

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Article name in the collection

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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Number of pages

15

Pages from-to

104-118

Publisher name

CEUR-WS

Place of publication

Bologna

Event location

Bologna

Event date

Sep 5, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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