A Dataset and Baselines for Multilingual Reply Suggestion

• Main Authors: Zhang, Mozhi, Wang, Wei, Deb, Budhaditya, Zheng, Guoqing, Shokouhi, Milad, Awadallah, Ahmed Hassan
• Format: Journal Article
• Language: English
• Published: 03.06.2021
• Subjects: Computer Science - Computation and Language; Computer Science - Learning
• DOI: 10.48550/arxiv.2106.02017

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.