A Dataset and Baselines for Multilingual Reply Suggestion

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 . Multilingual Reply Suggestion Automated reply suggestion is a useful feature for email and chat applications. Given an input message, the system suggests several replies, and users may click on them to save typing time (Figure 1 ). This feature is available in many applications including Gmail, Outlook, LinkedIn, Facebook Messenger, Microsoft Teams, and Uber. Reply suggestion is related to but different from open-domain dialog systems or chatbots (Adiwardana et al., 2020; Huang et al., 2020) . While both are conversational AI tasks (Gao et al., 2019) , the goals are different: reply suggestion systems help the user quickly reply to a message, while chatbots aim to continue the conversation and focus more on multi-turn dialogues. Ideally, we want our model to generate replies in any language. However, reply suggestion models require large training sets, so previous work mostly * Work mostly done as an intern at Microsoft Research.