\`It\`ak\'ur\`oso: Exploiting Cross-Lingual Transferability for Natural Language Generation of Dialogues in Low-Resource, African Languages

• URL: http://arxiv.org/abs/2204.08083v1
• Date: Sun, 17 Apr 2022 20:23:04 GMT
• Title: \`It\`ak\'ur\`oso: Exploiting Cross-Lingual Transferability for Natural Language Generation of Dialogues in Low-Resource, African Languages
• Authors: Tosin Adewumi, Mofetoluwa Adeyemi, Aremu Anuoluwapo, Bukola Peters, Happy Buzaaba, Oyerinde Samuel, Amina Mardiyyah Rufai, Benjamin Ajibade, Tajudeen Gwadabe, Mory Moussou Koulibaly Traore, Tunde Ajayi, Shamsuddeen Muhammad, Ahmed Baruwa, Paul Owoicho, Tolulope Ogunremi, Phylis Ngigi, Orevaoghene Ahia, Ruqayya Nasir, Foteini Liwicki and Marcus Liwicki
• Abstract summary: We investigate the possibility of cross-lingual transfer from a state-of-the-art (SoTA) deep monolingual model to 6 African languages. The languages are Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yorub'a. The results show that the hypothesis that deep monolingual models learn some abstractions that generalise across languages holds.
• Score: 0.9511471519043974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the possibility of cross-lingual transfer from a state-of-the-art (SoTA) deep monolingual model (DialoGPT) to 6 African languages and compare with 2 baselines (BlenderBot 90M, another SoTA, and a simple Seq2Seq). The languages are Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yor\`ub\'a. Generation of dialogues is known to be a challenging task for many reasons. It becomes more challenging for African languages which are low-resource in terms of data. Therefore, we translate a small portion of the English multi-domain MultiWOZ dataset for each target language. Besides intrinsic evaluation (i.e. perplexity), we conduct human evaluation of single-turn conversations by using majority votes and measure inter-annotator agreement (IAA). The results show that the hypothesis that deep monolingual models learn some abstractions that generalise across languages holds. We observe human-like conversations in 5 out of the 6 languages. It, however, applies to different degrees in different languages, which is expected. The language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78.1%, of which 34.4% are unanimous. The main contributions of this paper include the representation (through the provision of high-quality dialogue data) of under-represented African languages and demonstrating the cross-lingual transferability hypothesis for dialogue systems. We also provide the datasets and host the model checkpoints/demos on the HuggingFace hub for public access.

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