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https://doi.org/10.21256/zhaw-26147| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Improving NL-to-Query systems through re-ranking of semantic hypothesis |
| Authors: | von Däniken, Pius Deriu, Jan Milan Agirre, Eneko Brunner, Ursin Cieliebak, Mark Stockinger, Kurt |
| et. al: | No |
| DOI: | 10.21256/zhaw-26147 |
| Conference details: | 5th International Conference on Natural Language and Speech Processing (ICNLSP), online, 16-17 December 2022 |
| Issue Date: | Dec-2022 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Publisher / Ed. Institution: | Winterthur |
| Language: | English |
| Subjects: | Machine learning; Natural language processing; Database; User interface |
| Subject (DDC): | 005: Computer programming, programs and data 006: Special computer methods |
| Abstract: | Natural Language-to-Query systems translate a natural language question into a formal query language such as SQL. Typically the translation results in a set of candidate query statements due to the ambiguity of natural language. Hence, an important aspect of NL-to-Query systems is to rank the query statements so that the most relevant query is ranked on top. We propose a novel approach to significantly improve the query ranking and thus the accuracy of such systems. First, we use existing methods to translate the natural language question NL_in into k query statements and rank them. Then we translate each of the k query statements back into a natural language question NL_gen and use the semantic similarity between the original question NL_in and each of the k generated questions NL_gen to re-rank the output. Our experiments on two standard datasets, OTTA and Spider, show that this technique improves even strong state-of-the-art NL-to-Query systems by up to 9 percentage points. A detailed error analysis shows that our method correctly down-ranks queries with missing relations and wrong query types. While this work is focused on NL-to-Query, our method could be applied to any other semantic parsing problems as long as a text generation method is available. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26147 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Centre for Artificial Intelligence (CAI) Institute of Applied Information Technology (InIT) |
| Published as part of the ZHAW project: | INODE – Intelligent Open Data Exploration (EU Horizon 2020) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_vanDaeniken-etal_Improving-NL-to-Query-Systems_ICNLSP2022.pdf | 478.54 kB | Adobe PDF |  View/Open |
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