###### Abstract  
Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. Existing approaches mainly focus on English-centric directions and always underperform compared to their pivot-based counterparts for non-English directions. In this work, we aim to build a many-to-many MNMT system with an emphasis on the quality of non-English directions by exploring selective and aligned online data augmentation algorithms. Based on our findings showing that the augmented synthetic samples are not \"the more, the better\" we propose selective online back-translation (SOBT) and thoroughly study different selection criteria to pick suitable samples for training. Furthermore, we boost SOBT with cross-lingual online substitution (CLOS) to align token representations and encourage transfer learning. Our intuition is based on the hypothesis that a universal cross-lingual representation leads to a better multilingual translation performance, especially for non-English directions. Comparing to previous state-of-the-art many-to-many MNMT models and conventional pivot-based methods, experiments on IWSLT2014 and OPUS-100 translation benchmarks show that our approach achieves a competitive or even better performance on English-centric directions and achieves up to \(\sim\)12 BLEU for non-English directions. All of our models and codes are publicly available. mary-to-many translation; back translation; zero-shot translation; data augmentation; quality estimation; alignment +
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where MNMT models tend to translate into a wrong target language or simply copy the source sentence in zero-shot directions. As back-translation has aroused more and more interests for improving NMT [23; 24; 25], a commonly used method for improving massively MNMT is to generate synthetic sentence pairs for all language directions and train a many-to-many MNMT model from scratch on the merged datasets of both real and synthetic sentence pairs [16; 17]. As decoding the whole training set for all language directions makes the scalability to massively MNMT questionable, [PERSON] et al. [21] proposed the random online back-translation (ROBT) algorithm, fine-tuning a pre-trained MNMT model for unseen language pairs with pseudo sentence pairs generated by back-translating the target-side sentences during training. However, the vanilla ROBT algorithm generates pool sentence pairs in non-English directions intuitively and has a low upper bound for improving multilingual translation. Some other researchers reported improving massively MNMT performance with data augmentation by building code-switched sentence pairs [26; 27]. They generated augmented samples on both parallel and monolingual data by replacing words with the same meaning in synonym dictionaries. However, prepared synonym dictionaries or word alignment models are needed in these methods, which are always unreachable for non-English directions. In this paper, we mainly focus on improving the translation quality of massively MNMT systems, in particular non-English directions. We explore reasons and solutions for two specific problems that still remain unsolved:  
* Low-quality synthetic sentence pairs, such as samples generated by the vanilla ROBT algorithm, are not very useful for many-to-many MNMT models. Therefore, how can we generate suitable augmented samples to deliver greater and continued benefits? * Another limitation of many-to-many MNMT is the language variant encoder representations, which means the encoder representations of parallel sentences generated by the model are dissimilar. Therefore, how can we align token representations and encourage transfer learning utilizing synthetic sentences? We address these problems by investigating selective and aligned online data augmentation algorithms.