NOVOTNÝ, Vít and Michal ŠTEFÁNIK. Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval). Online. In Guglielmo Faggioli, Nicola Ferro, Allan Hanbury, Martin Potthast. Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR-WS, 2022, p. 104-118. ISSN 1613-0073.
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Basic information
Original name Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)
Authors NOVOTNÝ, Vít (203 Czech Republic, guarantor, belonging to the institution) and Michal ŠTEFÁNIK (703 Slovakia, belonging to the institution).
Edition Bologna, Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, p. 104-118, 15 pp. 2022.
Publisher CEUR-WS
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Italy
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW fulltext PDF
RIV identification code RIV/00216224:14330/22:00126431
Organization unit Faculty of Informatics
ISSN 1613-0073
Keywords in English information retrieval; sparse retrieval; dense retrieval; soft vector space model; math representations; word embeddings; constrained positional weighting; decontextualization; word2vec; transformers
Tags evaluation, information retrieval, math indexing and retrieval, math information retrieval, MathML, Positional embeddings, ranking, SCM, similarity search, soft cosine measure
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 6/4/2023 09:35.
Abstract
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.
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