mlsanityData to 7 Oct 2026

A Dataset and Baselines for Multilingual Reply Suggestion

arXiv 2106.02017 ↗First posted 3 Jun 2021cs.CL cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 55 of its 74 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

55 of 74 references.

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 1; 2025: 2.

9 papers.

  1. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  2. 2022MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languagescited by 13
  3. 2023xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmarkcited by 13
  4. 2025Efficient Compositional Multi-tasking for On-device Large Language Modelscited by 4
  5. 2025K-Merge: Online Continual Merging of Adapters for On-device Large Language Modelscited by 3
  6. 2021A Conditional Generative Matching Model for Multi-lingual Reply Suggestioncited by 1
  7. 2023Is Translation Helpful? An Empirical Analysis of Cross-Lingual Transfer in Low-Resource Dialog Generationcited by 0
  8. 2023Model-Based Simulation for Optimising Smart Replycited by 0
  9. 2024Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedbackcited by 0