Paradigm Completion for Derivational Morphology
This addresses a gap in NLP for linguists and language modelers, but it is incremental as it adapts existing methods to a new task.
The paper tackled the overlooked problem of generating complex derived word forms in NLP by applying neural sequence-to-sequence models, achieving a 16.4% improvement over a non-neural baseline.
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.