OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context

• URL: http://arxiv.org/abs/2407.15736v1
• Date: Mon, 22 Jul 2024 15:40:17 GMT
• Title: OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context
• Authors: Steffen Kleinle, Jakob Prange, Annemarie Friedrich,
• Abstract summary: We present OMoS-QA, a dataset of German and English questions paired with trustworthy documents and manually annotated answers. Questions are automatically generated with an open-source large language model (LLM) and answer sentences are selected by crowd workers. We find high precision and low-to-mid recall in selecting answer sentences, which is a favorable trade-off to avoid misleading users.
• Score: 4.39796591456426
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When immigrating to a new country, it is easy to feel overwhelmed by the need to obtain information on financial support, housing, schooling, language courses, and other issues. If relocation is rushed or even forced, the necessity for high-quality answers to such questions is all the more urgent. Official immigration counselors are usually overbooked, and online systems could guide newcomers to the requested information or a suitable counseling service. To this end, we present OMoS-QA, a dataset of German and English questions paired with relevant trustworthy documents and manually annotated answers, specifically tailored to this scenario. Questions are automatically generated with an open-source large language model (LLM) and answer sentences are selected by crowd workers with high agreement. With our data, we conduct a comparison of 5 pretrained LLMs on the task of extractive question answering (QA) in German and English. Across all models and both languages, we find high precision and low-to-mid recall in selecting answer sentences, which is a favorable trade-off to avoid misleading users. This performance even holds up when the question language does not match the document language. When it comes to identifying unanswerable questions given a context, there are larger differences between the two languages.

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