A Dataset and Baselines for Multilingual Reply Suggestion

Abstract

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs.

arXiv commentACL 2021

References in corpus (11)

• Sequence to Sequence Learning with Neural Networks
• Overcoming catastrophic forgetting in neural networks
• Language Models are Few-Shot Learners
• Multilingual Denoising Pre-training for Neural Machine Translation
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• mT5: A massively multilingual pre-trained text-to-text transformer
• Towards a Human-like Open-Domain Chatbot
• Efficient Natural Language Response Suggestion for Smart Reply
• Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
• Smart Reply: Automated Response Suggestion for Email
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models