Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection

This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the inflected form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperform the next best competitor.Comment: To appear in Proceedings of the 15th Annual SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology at CoNLL 201

Weird inflects but OK : Making sense of morphological generation errors

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. © 2019 Association for Computational Linguistics.Peer reviewe

SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models

This paper describes the Stockholm University/University of Groningen (SU-RUG) system for the SIGMORPHON 2017 shared task on morphological inflection. Our system is based on an attentional sequence-to-sequence neural network model using Long Short-Term Memory (LSTM) cells, with joint training of morphological inflection and the inverse transformation, i.e. lemmatization and morphological analysis. Our system outperforms the baseline with a large margin, and our submission ranks as the 4th best team for the track we participate in (task 1, high-resource).Comment: 4 pages, to appear at CoNLL-SIGMORPHON 201

Automated Learning of Hungarian Morphology for Inflection Generation and Morphological Analysis

The automated learning of morphological features of highly agglutinative languages is an important research area for both machine learning and computational linguistics. In this paper we present a novel morphology model that can solve the inflection generation and morphological analysis problems, managing all the affix types of the target language. The proposed model can be taught using (word, lemma, morphosyntactic tags) triples. From this training data, it can deduce word pairs for each affix type of the target language, and learn the transformation rules of these affix types using our previously published, lower-level morphology model called ASTRA. Since ASTRA can only handle a single affix type, a separate model instance is built for every affix type of the target language. Besides learning the transformation rules of all the necessary affix types, the proposed model also calculates the conditional probabilities of the affix type chains using relative frequencies, and stores the valid lemmas and their parts of speech. With these pieces of information, it can generate the inflected form of input lemmas based on a set of affix types, and analyze input inflected word forms. For evaluation, we use Hungarian data sets and compare the accuracy of the proposed model with that of state of the art morphology models published by SIGMORPHON, including the Helsinki (2016), UF and UTNII (2017), Hamburg, IITBHU and MSU (2018) models. The test results show that using a training data set consisting of up to 100 thousand random training items, our proposed model outperforms all the other examined models, reaching an accuracy of 98% in case of random input words that were not part of the training data. Using the high-resource data sets for the Hungarian language published by SIGMORPHON, the proposed model achieves an accuracy of about 95-98%