text: A common intermediate language representation or an interlingua is the holy grail in machine translation. Thanks to the new neural machine translation approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new architecture based on introducing an interlingua loss as an additional training objective. By adding and forcing this interlingua loss, we are able to train multiple encoders and decoders for each language, sharing a common intermediate representation. Preliminary translation results on the WMT Turkish English and WMT 2019 Kazakh English tasks show improvements over the baseline system. Additionally, since the final objective of our architecture is having compatible encoder decoders based on a common representation, we visualize and evaluate the learned intermediate representations. What is most relevant from our study is that our architecture shows the benefits of the dreamed interlingua since it is capable of: (1) reducing the number of production systems, with respect to the number of languages, from quadratic to linear (2) incrementally adding a new language in the system without retraining languages previously there and (3) allowing for translations from the new language to all the others present in the system
  @cite_28: We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a true interlingua by performing direct zero-shot translation (without using pivot translation), and by using the interlingual sentence embeddings to train an English Yelp review classifier that, through the mediation of the interlingua, can also classify French and German reviews. Furthermore, we show that, despite using a smaller number of parameters than a pairwise collection of bilingual NMT models, our interlingual approach produces comparable BLEU scores for each language pair in WMT15.
  @cite_26: Presently there has been a surge for representing information in a common language for use cases like question-answering system, machine translation, text summarization etc. UNL has been a centre of attraction for researchers in the past two decades and many have tried to harness its power. This paper throws a glimpse into the strides made by such researchers. As UNL provides a language independent platform it eases out the decision making by accessing the valuable and meaningful information, which is otherwise a challenging errand. Thus it captures information and finds its major application in Natural Language Processing (NLP) domain. The paper is written with the intent to introduce its readers to the UNL framework and explain how it is being used to solve some real world problems.
  @cite_7: 
summary: Classical interlingua approaches @cite_28 aim at finding a universal or common language representation that involves a conceptual understanding of all languages over the world. This has been the case of Esperanto @cite_7 or Universal Networking Language @cite_26 and many others. Very differently, in this work, we are focusing on training a common language representation with deep learning techniques. The objective is to train an intermediate representation that allows using independent encoders and decoders for each language. In this scenario, translation systems in a highly multilingual environment get reduced from quadratic to linear and also, translation is available for languages pairs that have not been explicitly trained. Differently, from the classical approach, there is no requirement of semantics for this intermediate representation. Following a similar objective or methodology, most related works are the following ones.