Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics

Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type…

Word Representation Models for Morphologically Rich Languages in Neural Machine Translation

Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most modern neural systems of translation, whi…

Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs

We present extensions to a continuous-state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high-performance transition-based parser that uses long short-term memory …

Compositional Representation of Morphologically-Rich Input for Neural Machine Translation

Neural machine translation (NMT) models are typically trained with fixed-size input and output vocabularies, which creates an important bottleneck on their accuracy and generalization capability. As a solution, various s…

Parsing Morphologically Complex Words