A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
PSU at CLEF-2020 ARQMath Track: Unsupervised Re-ranking using Pretraining
2020
Conference and Labs of the Evaluation Forum
This paper elaborates on our submission to the ARQMath track at CLEF 2020. Our primary run for the main Task-1: Question Answering uses a two-stage retrieval technique in which the first stage is a fusion of traditional BM25 scoring and tf-idf with cosine similarity-based retrieval while the second stage is a finer re-ranking technique using contextualized embeddings. For the re-ranking we use a pre-trained robertabase model (110 million parameters) to make the language model more math-aware.
dblp:conf/clef/Rohatgi0G20
fatcat:76djkwmfejbeva5e7j2y74irve