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

Understand

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 (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup-based word representations with representations constructed from the orthographic representations of the words, also using LSTMs.
• This allows statistical sharing across word forms that are similar on the surface.
• Experiments for morphologically rich languages show that the parsing model benefits from incorporating the character-based encodings of words.