Morphological Inflection Generation with Hard Monotonic Attention
| Autor: | Roee Aharoni, Yoav Goldberg |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry Monotonic function Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Task (project management) Inflection 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Artificial intelligence business Computation and Language (cs.CL) Mechanism (sociology) Word (computer architecture) 0105 earth and related environmental sciences |
| Zdroj: | ACL (1) |
| DOI: | 10.18653/v1/p17-1183 |
| Popis: | We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various setups compared to previous neural and non-neural approaches. Finally we present an analysis of the continuous representations learned by both the hard and soft attention \cite{bahdanauCB14} models for the task, shedding some light on the features such models extract. Accepted as a long paper in ACL 2017 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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