Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text
This addresses the challenge of lemmatization for low-resource languages by reducing dependency on annotated corpora, though it is incremental as it builds on existing methods with new data combinations.
The paper tackled the problem of training context-sensitive lemmatizers in low-resource languages where labeled data is scarce, by using inflection tables and raw text to augment training data, resulting in improved overall performance and especially on unseen words compared to a baseline.
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in low-resource languages. In addition (as shown here), in a low-resource setting, a lemmatizer can learn more from $n$ labeled examples of distinct words (types) than from $n$ (contiguous) labeled tokens, since the latter contain far fewer distinct types. To combine the efficiency of type-based learning with the benefits of context, we propose a way to train a context-sensitive lemmatizer with little or no labeled corpus data, using inflection tables from the UniMorph project and raw text examples from Wikipedia that provide sentence contexts for the unambiguous UniMorph examples. Despite these being unambiguous examples, the model successfully generalizes from them, leading to improved results (both overall, and especially on unseen words) in comparison to a baseline that does not use context.