MTG: A Benchmark Suite for Multilingual Text Generation

By Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Lei Li. 2021

26 citations recorded by the source corpus.

We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at \url{https://github.com/zide05/MTG}.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks, Natural Language Generation

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Hinrich Schütze

Citations in the corpus

LongForm: Effective Instruction Tuning with Reverse Instructions (2023, positive): “Furthermore, we demonstrate that our LongForm models – instruction-tuned on LongForm-C– outperform prior instruction-tuned baselines such as FLAN-T5 and Alpaca by a big margin on a wide variety of long text generation tasks.”
mGPT: Few-Shot Learners Go Multilingual (2022, background): “The benefit of balancing the et al., 2022; Biderman et al., 2023; Dey et al., 2023), Chinese (Zeng et al., 2021), and Russian (Zmitrovich et al., 2023).”