  We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale
collection of Amazon reviews for multilingual text classification. The corpus
contains reviews in English, Japanese, German, French, Spanish, and Chinese,
which were collected between 2015 and 2019. Each record in the dataset contains
the review text, the review title, the star rating, an anonymized reviewer ID,
an anonymized product ID, and the coarse-grained product category (e.g.,
'books', 'appliances', etc.) The corpus is balanced across the 5 possible star
ratings, so each rating constitutes 20% of the reviews in each language. For
each language, there are 200,000, 5,000, and 5,000 reviews in the training,
development, and test sets, respectively. We report baseline results for
supervised text classification and zero-shot cross-lingual transfer learning by
fine-tuning a multilingual BERT model on reviews data. We propose the use of
mean absolute error (MAE) instead of classification accuracy for this task,
since MAE accounts for the ordinal nature of the ratings.
