Don't put volumes

NLG Task: <br/><br/> Previous work only studies English reply suggestion. So, this paper presents MRS, a Multilingual Reply suggestion dataset with ten languages. <br/>The task is to build a generation and a retrieval model as baselines for MRS. <br/> MRS is publicly available at https://github.com/zhangmozhi/mrs <br/> <br/> Training Data: <br/><br/> The Data used to investigate reply suggestion is from a dataset dubbed MRS built from publicly available Reddit threads. Message reply pairs, response sets, and machine translated examples from ten languages are extracted </br> For each language, we use 80% examples for training, 10% for validation, and 10% for testing. We then create response sets for retrieval models. <br/><br/> Model Description <br/><br/> Retrieval Model -> Selects the reply from a predetermined response set. Easier to train, run faster and a curated response set guarantees the coherence and the safety of the model output <br/><br/> Generation Model -> Produce replys from scratch and are more powerful because they are not constrained by the response set <br/><br/> Key Contributions: <br/><br/> This paper demonstrates that the generation model beats the retrieval model in nearly every language set, resulting in higher relevance scores and more diverse replies. </br></br> However, However, unlike the retrieval model, the generation model fails to generalize across languages in the zero-shot setting, despite using Unicoder-XDAE for initialization. The generation model "forgets" its multilingual knowledge acquired during pre-training. This suggests that reply suggestion poses unique challenges for cross-lingual transfer learning, <br/><br/> Results: <br/><br/>