Learning non-concatenative morphology

Adaptor Grammars for Learning Non-Concatenative Morphology

Unsupervised acquisition of concatenative morphology

Among the linguistic resources formalizing a language, morphological rules are among those that can be achieved in a reasonable time. Nevertheless, since the construction of such resource can require linguistic expertise…

How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?

Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology. We d…

Splintering Nonconcatenative Languages for Better Tokenization

Common subword tokenization algorithms like BPE and UnigramLM assume that text can be split into meaningful units by concatenative measures alone. This is not true for languages such as Hebrew and Arabic, where morpholog…

Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem th…