![]() The predominant method to tackle this problem is Byte Pair Encoding (BPE) which splits words, including OOV words, into sub-word segments. out-of-vocabulary (OOV) words) have long been a fundamental challenge for NMT systems. As a result, dealing with the words not occurring during training (a.k.a. Neural Machine Translation (NMT) is an open vocabulary problem. Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track) ![]() How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation? Our results on the WMT Romanian-English and English-Turkish benchmarks show such transfer leads to the best-performing continuous model. We explore pretrained embeddings and also introduce knowledge transfer from the discrete Transformer model using embeddings in Euclidean and non-Euclidean spaces. We argue that the choice of embeddings is crucial for such models, so we aim to focus on one particular aspect”:" target representation via embeddings. In this work, we study continuous text generation using Transformers for neural machine translation (NMT). However, continuous models for text generation have received limited attention from the community. Continuous generative models proved their usefulness in high-dimensional data, such as image and audio generation.
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