AfriWOZ: Corpus for Exploiting Cross-Lingual Transfer for Dialogue Generation in Low-Resource, African Languages

Dialogue generation is an important NLP task fraught with many challenges. The challenges become more daunting for low-resource African languages. To enable the creation of dialogue agents for African languages, we contribute the first high-quality dialogue datasets for 6 African languages: Swahili,...

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Main Authors: Adewumi, Tosin, Adeyemi, Mofetoluwa, Anuoluwapo, Aremu, Peters, Bukola, Buzaaba, Happy, Samuel, Oyerinde, Rufai, Amina Mardiyyah, Ajibade, Benjamin, Gwadabe, Tajudeen, Koulibaly Traore, Mory Moussou, Ajayi, Tunde Oluwaseyi, Muhammad, Shamsuddeen, Baruwa, Ahmed, Owoicho, Paul, Ogunremi, Tolulope, Ngigi, Phylis, Ahia, Orevaoghene, Nasir, Ruqayya, Liwicki, Foteini, Liwicki, Marcus
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Benchmark testing
crosslingual
dialogue systems
low-resource
Machine Learning
Maskininlärning
multilingual
Neural networks
NLG
Oral communication
Task analysis
Training
Transfer learning
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Summary:Dialogue generation is an important NLP task fraught with many challenges. The challenges become more daunting for low-resource African languages. To enable the creation of dialogue agents for African languages, we contribute the first high-quality dialogue datasets for 6 African languages: Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yorùbá. There are a total of 9,000 turns, each language having 1,500 turns, which we translate from a portion of the English multi-domain MultiWOZ dataset. Subsequently, we benchmark by investigating & analyzing the effectiveness of modelling through transfer learning by utilziing state-of-the-art (SoTA) deep monolingual models: DialoGPT and BlenderBot. We compare the models with a simple seq2seq baseline using perplexity. Besides this, we conduct human evaluation of single-turn conversations by using majority votes and measure inter-annotator agreement (IAA). We find that the hypothesis that deep monolingual models learn some abstractions that generalize across languages holds. We observe human-like conversations, to different degrees, in 5 out of the 6 languages. 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. We freely provide the datasets and host the model checkpoints/demos on the HuggingFace hub for public access.
ISSN:2161-4407
DOI:10.1109/IJCNN54540.2023.10191208