INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages

• URL: http://arxiv.org/abs/2502.09814v1
• Date: Thu, 13 Feb 2025 23:17:10 GMT
• Title: INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages
• Authors: Hao Yu, Jesujoba O. Alabi, Andiswa Bukula, Jian Yun Zhuang, En-Shiun Annie Lee, Tadesse Kebede Guge, Israel Abebe Azime, Happy Buzaaba, Blessing Kudzaishe Sibanda, Godson K. Kalipe, Jonathan Mukiibi, Salomon Kabongo Kabenamualu, Mmasibidi Setaka, Lolwethu Ndolela, Nkiruka Odu, Rooweither Mabuya, Shamsuddeen Hassan Muhammad, Salomey Osei, Sokhar Samb, Juliet W. Murage, Dietrich Klakow, David Ifeoluwa Adelani,
• Abstract summary: Slot-filling and intent detection are well-established tasks in Conversational AI. We introduce Injongo -- a multicultural, open-source benchmark dataset for 16 African languages. We show the advantage of leveraging African-cultural utterances over Western-centric utterances for improving cross-lingual transfer.
• Score: 15.983678567785004
• License:
• Abstract: Slot-filling and intent detection are well-established tasks in Conversational AI. However, current large-scale benchmarks for these tasks often exclude evaluations of low-resource languages and rely on translations from English benchmarks, thereby predominantly reflecting Western-centric concepts. In this paper, we introduce Injongo -- a multicultural, open-source benchmark dataset for 16 African languages with utterances generated by native speakers across diverse domains, including banking, travel, home, and dining. Through extensive experiments, we benchmark the fine-tuning multilingual transformer models and the prompting large language models (LLMs), and show the advantage of leveraging African-cultural utterances over Western-centric utterances for improving cross-lingual transfer from the English language. Experimental results reveal that current LLMs struggle with the slot-filling task, with GPT-4o achieving an average performance of 26 F1-score. In contrast, intent detection performance is notably better, with an average accuracy of 70.6%, though it still falls behind the fine-tuning baselines. Compared to the English language, GPT-4o and fine-tuning baselines perform similarly on intent detection, achieving an accuracy of approximately 81%. Our findings suggest that the performance of LLMs is still behind for many low-resource African languages, and more work is needed to further improve their downstream performance.

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