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The ARQMath Lab at CLEF 2020 considers the problem of finding answers to new mathematical questions among posted answers on a community question answering site (Math Stack Exchange). Queries are question postings held out from the test collection, each containing both text and at least one formula. We expect this to be a challenging task, as both math and text may be needed to find relevant answer posts. While several models have been proposed for text question answering, math question answering is in an earlier stage of development. To advance math-aware search and mathematical question answering systems, we will create a standard test collection for researchers to use for benchmarking. ARQMath will also include a formula retrieval sub-task: individual formulas from question posts are used to locate formulas in earlier answer posts, with relevance determined by narrative fields created based on the original question. We will use these narrative fields to explore diverse information needs for formula search (e.g., alternative notation, applications in specific fields or definition).
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Exploring Diversification In Non-factoid Question Answering
Retrieving short, precise answers to non-factoid queries is an increasingly important task, especially for mobile and voice search. Many of these questions may have multiple or alternative answers. In an environment where answers are presented incrementally, this raises the question of how to generate a diverse ranking to cover these alternatives. Existing search diversification algorithms generate diverse document rankings using explicit or implicit methods based on topical similarity. The goal of this paper is to evaluate the impact of applying these existing document diversification frameworks to the problem of answer diversification to determine if topical diversity is related to answer diversity. Using two common diversification algorithms, xQUAD and PM-2, and three question answering test collections, we show that topic diversification can help to generate more effective rankings but is not consistent across different queries and test collections.
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- Award ID(s):
- 1715095
- PAR ID:
- 10090130
- Date Published:
- Journal Name:
- Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval
- Page Range / eLocation ID:
- 223 to 226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3234944.3234973
