This paper presents the submission of the Linguistics Department of the University of Colorado at Boulder for the 2017 CoNLL-SIGMORPHON Shared Task on Universal Morphological Reinflection. The system is implemented as an RNN Encoder-Decoder. It is specifically geared toward a low-resource setting. To this end, it employs data augmentation for counteracting overfitting and a copy symbol for processing characters unseen in the training data. The system is an ensemble of ten models combined using a weighted voting scheme. It delivers substantial improvement in accuracy compared to a non-neural baseline system in presence of varying amounts of training data.

We conduct a manual error analysis of the CoNLL-SIGMORPHON 2017 Shared Task on Morphological Reinflection. In this task, systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). This design lets us analyze errors much like we might analyze children's production errors. We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual "errors" reflect errors in the gold data.

We present a corpus of Finnish news articles with a manually prepared named entity annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source. The corpus is available for research purposes. We present baseline experiments on the corpus using a rule-based and two deep learning systems on two, in-domain and out-of-domain, test sets.

This paper describes FinnPos, an open-source morphological tagging and lemmatization toolkit for Finnish. The morphological tagging model is based on the averaged structured perceptron classifier. Given training data, new taggers are estimated in a computationally efficient manner using a combination of beam search and model cascade. The lemmatization is performed employing a combination of a rule-based morphological analyzer, OMorFi, and a data-driven lemmatization model. The toolkit is readily applicable for tagging and lemmatization of running text with models learned from the recently published Finnish Turku Dependency Treebank and FinnTreeBank. Empirical evaluation on these corpora shows that FinnPos performs favorably compared to reference systems in terms of tagging and lemmatization accuracy. In addition, we demonstrate that our system is highly competitive with regard to computational efficiency of learning new models and assigning analyses to novel sentences.

This paper presents initial experiments in data-driven morphological analysis for Finnish using deep learning methods. Our system uses a character based bidirectional LSTM and pretrained word embeddings to predict a set of morphological analyses for an input word form. We present experiments on morphological analysis for Finnish. We learn to mimic the output of the OMorFi analyzer on the Finnish portion of the Universal Dependency treebank collection. The results of the experiments are encouraging and show that the current approach has potential to serve as an extension to existing rule-based analyzers. Tiivistelmä Esittelemme kokeita aineistolähtöisellä syväoppimismenetelmiin perustuvalla suomen kielen morfologisella analysaattorilla. Esittelemämme järjestelmä perustuu merkkipohjaisiin LSTM-malleihin ja esiopetettuihin sanaupotuksiin. Järjestelmämme oppii matkimaan OMorFi-jäsennintä, joka on suomen kielen morfologinen analysaattori. Teemme kokeita Universal Dependency-puupankkikokoelman suomenkielisellä osuudella. Kokeemme osoittavat, että koneoppimismenetelmät tarjoavat lupaavan lähestymistavan suomen kielen morfologiseen analyysiin.