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

NOVOTNÝ, Vít a 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. s. 104-118. ISSN 1613-0073. [citováno 2024-04-24]

Základní údaje

• Originální název: Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)
• Autoři: NOVOTNÝ, Vít (203 Česká republika, garant, domácí) a Michal ŠTEFÁNIK (703 Slovensko, domácí)
• Vydání: Bologna, Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, od s. 104-118, 15 s. 2022.
• Nakladatel: CEUR-WS

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Stať ve sborníku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Itálie
• Utajení: není předmětem státního či obchodního tajemství
• Forma vydání: elektronická verze "online"
• WWW: fulltext PDF
• Kód RIV: RIV/00216224:14330/22:00126431
• Organizační jednotka: Fakulta informatiky
• ISSN: 1613-0073
• Klíčová slova anglicky: information retrieval; sparse retrieval; dense retrieval; soft vector space model; math representations; word embeddings; constrained positional weighting; decontextualization; word2vec; transformers
• Štítky: evaluation, information retrieval, math indexing and retrieval, math information retrieval, MathML, Positional embeddings, ranking, SCM, similarity search, soft cosine measure
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.