This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI). The effect of language relatedness on MNMT model efficiency is studied. Owing to the presence of large corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages. Results reveal that using related languages is beneficial for the WI group only, while it is detrimental for the EI group and shows an inconclusive effect on the DR group, but it is useful for EN-IL models. Thus, related language groups are used to develop pivot MNMT models. Furthermore, the IL corpora are transliterated from the corresponding scripts to a modified ITRANS script, and the best MNMT models from the previous approaches are built on the transliterated corpus. It is observed that the usage of pivot models greatly improves MNMT baselines with AS-TA achieving the minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS, ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the best. Transliteration also helps the models with few exceptions. The best increment of scores is observed in ML, TA, and BN and the worst average increment is observed in KN, HI, and PA, across all languages. The best model obtained is the PA-HI language pair trained on PAWI transliterated corpus which gives 24.29 BLEU.

翻译：本文提出了一种基于Samanantar语料库实现、并在Flores-200语料库上分析的11种印度语言(IL)间的印地-印地(IL-IL)多语言神经机器翻译(MNMT)基线模型。所有模型均采用BLEU分数进行评估。此外，语言被分为东印度-雅利安(EI)、达罗毗荼(DR)和西印度-雅利安(WI)三个语系组别，并研究了语言亲缘关系对MNMT模型效率的影响。鉴于存在大量英语(EN)到IL的平行语料，本研究还构建并考察了以英语为枢轴语言的MNMT IL-IL模型。为此，同时开发了使用和不使用亲缘语言的英语-印地(EN-IL)模型。结果表明：使用亲缘语言仅对WI组有利，对EI组存在负面影响，对DR组效果不明确，但对EN-IL模型具有正向作用。因此，研究利用亲缘语言组构建了枢轴MNMT模型。进一步地，将IL语料从对应文字系统转写为改进版ITRANS脚本，并在转写语料上构建了前述方法中的最优MNMT模型。实验发现：使用枢轴模型显著提升了MNMT基线性能，其中AS-TA语言对获得最低BLEU分数，PA-HI获得最高分数；在具体语言中，AS、ML和TA的BLEU分数最低，而HI、PA和GU表现最佳；除少数例外情况，文字转写对模型性能亦有提升作用。所有语言中，ML、TA和BN的分数增幅最大，KN、HI和PA的平均增幅最小。最优模型为基于PAWI转写语料训练的PA-HI语言对，其BLEU分数达到24.29。