  Our book "The Reality of Multi-Lingual Machine Translation" discusses the
benefits and perils of using more than two languages in machine translation
systems. While focused on the particular task of sequence-to-sequence
processing and multi-task learning, the book targets somewhat beyond the area
of natural language processing. Machine translation is for us a prime example
of deep learning applications where human skills and learning capabilities are
taken as a benchmark that many try to match and surpass. We document that some
of the gains observed in multi-lingual translation may result from simpler
effects than the assumed cross-lingual transfer of knowledge.
  In the first, rather general part, the book will lead you through the
motivation for multi-linguality, the versatility of deep neural networks
especially in sequence-to-sequence tasks to complications of this learning. We
conclude the general part with warnings against too optimistic and unjustified
explanations of the gains that neural networks demonstrate.
  In the second part, we fully delve into multi-lingual models, with a
particularly careful examination of transfer learning as one of the more
straightforward approaches utilizing additional languages. The recent
multi-lingual techniques, including massive models, are surveyed and practical
aspects of deploying systems for many languages are discussed. The conclusion
highlights the open problem of machine understanding and reminds of two ethical
aspects of building large-scale models: the inclusivity of research and its
ecological trace.
