A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

Reinforcement learning (RL) is frequently used to increase performance in text generation
tasks, including machine translation (MT), notably through the use of Minimum Risk Training …

Deep learning sequence models have been successfully applied to the task of
morphological inflection. The results of the SIGMORPHON shared tasks in the past several …

Automatic morphological processing can aid downstream natural language processing
applications, especially for low-resource languages, and assist language documentation …

We employ imitation learning to train a neural transition-based string transducer for
morphological tasks such as inflection generation and lemmatization. Previous approaches …

We propose the task of unsupervised morphological paradigm completion. Given only raw
text and a lemma list, the task consists of generating the morphological paradigms, ie, all …

Canonical morphological segmentation consists of dividing words into their standardized
morphemes. Here, we are interested in approaches for the task when training data is limited …

While achieving state-of-the-art results in multiple tasks and languages, translation-based
cross-lingual transfer is often overlooked in favour of massively multilingual pre-trained …

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 …

This paper presents the submissions by the University of Zurich to the CoNLL–
SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection. Our system is …