arXiv:1512.06110
공식 코드 · Papers with Code
Morphological Inflection Generation Using Character Sequence to Sequence Learning
원문 초록
Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
Making Language Model a Hierarchical Classifier and Generator
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
토론
이 논문과 연결된 포럼 글이 아직 없습니다.