Nearest Neighbors

Translation models often fail to generate good translations for infrequent words or phrases. Previous work attacked this problem by in- ducing new translation rules from monolin- gual data with a …...

AbstractIn this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach supports language variants and dialects with very limited parallel …...

Earlier approaches indirectly studied the information captured by the hidden states of recurrent and non-recurrent neural ma- chine translation models by feeding them into different classifiers. In this paper, we …...

AbstractContextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we …...

AbstractNeural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character embedding matrices. …...

AbstractNamed Entity Recognition (NER) is an essential component of many Natural Language Processing pipelines. However, building these language dependent models requires large amounts of annotated data. Crowdsourcing emerged as a …...

AbstractRecently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search …...

AbstractPhrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness. In this paper, we propose a …...