D eep SPIN : Deep Structured Prediction for Natural Language Processing (2020)

Abstract: DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation applications.

## description neural network models became the standard in nlp applications, with impressive results in machine translation @xcite . new language interfaces (digital assistants, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines. from a machine learning perspective, many problems in nlp can be characterized as structured prediction: they involve predicting structurally rich and interdependent outputs. in spite of this, current neural nlp systems ignore the structural complexity of human language, relying on simplistic and errorprone greedy search procedures. this leads to critical mistakes in mt, such as words being dropped or named entities mistranslated. the deepspin project attacks these fundamental problems by bringing together deep learning and structured prediction. this is done in three fronts: better generation strategies, beyond left-toright search; induction of sparse latent structure to c 2020 the authors. this article is licensed under a creative commons 3.0 licence, no derivative works, attribution, cc-by-nd. 1 project website: https://deep-spin.github.io . make networks more interpretable; and incorporation of weak supervision to reduce the need for labeled data. we focus here on the applications to machine translation, including some results that have already been obtained in the project.