The SweLL Language Learner Corpus

The article presents a new language learner corpus for Swedish, SweLL, and the methodology from collection and pesudonymisation to protect personal information of learners to annotation adapted to second language learning. The main aim is to deliver a well-annotated corpus of essays written by second language learners of Swedish and make it available for research through a browsable environment. To that end, a new annotation tool and a new project management tool have been implemented, – both with the main purpose to ensure reliability and quality of the final corpus. In the article we discuss reasoning behind metadata selection, principles of gold corpus compilation and argue for separation of normalization from correction annotation.

Special Issue of Selected Contributions from the Seventh Swedish Language Technology Conference (SLTC 2018)

This Special Issue contains three papers that are extended versions of abstracts presented at the Seventh Swedish Language Technology Conference (SLTC 2018), held at Stockholm University 8-9 November 2018.1 SLTC 2018 received 34 submissions, of which 31 were accepted for presentation. The number of registered participants was 113, including both attendees at SLTC 2018 and two co-located workshops that took place on 7 November. 32 participants were internationally affiliated, of which 14 were from outside the Nordic countries. Overall participation was thus on a par with previous editions of SLTC, but international participation was higher.

The Koala Part-of-Speech Tagset for Written Swedish

We present the Koala part-of-speech tagset for written Swedish. The categorization takes the Swedish Academy grammar (SAG) as its main starting point, to fit with the current descriptive view on Swedish grammar. We argue that neither SAG, as is, nor any of the existing part-of-speech tagsets, meet our requirements for a broadly applicable categorization. Our proposal is outlined and compared to the other descriptions, and motivations for both the tagset as a whole as well as decisions about individual tags are discussed.

The Interplay Between Loss Functions and Structural Constraints in Dependency Parsing

Dependency parsing can be cast as a combinatorial optimization problem with the objective to find the highest-scoring graph, where edge scores are learnt from data. Several of the decoding algorithms that have been applied to this task employ structural restrictions on candidate solutions, such as the restriction to projective dependency trees in syntactic parsing, or the restriction to noncrossing graphs in semantic parsing. In this paper we study the interplay between structural restrictions and a common loss function in neural dependency parsing, the structural hingeloss. We show how structural constraints can make networks trained under this loss function diverge and propose a modified loss function that solves this problem. Our experimental evaluation shows that the modified loss function can yield improved parsing accuracy, compared to the unmodified baseline.

Part of Speech Tagging: Shallow or Deep Learning?

Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.

Foreword to the Special Issue on Uralic Languages

In this introduction we have tried to present concisely the history of language technology for Uralic languages up until today, and a bit of a desiderata from the point of view of why we organised this special issue. It is of course not possible to cover everything that has happened in a short introduction like this. We have attempted to cover the beginnings of the (Uralic) language-technology scene in 1980’s as far as it’s relevant to much of the current work, including the ones presented in this issue. We also go through the Uralic area by the main languages to survey on existing resources, to also form a systematic overview of what is missing. Finally we talk about some possible future directions on the pan-Uralic level of language technology management.

Utilizing Language Technology in the Documentation of Endangered Uralic Languages

The paper describes work-in-progress by the Pite Saami, Kola Saami and Izhva Komi language documentation projects, all of which record new spoken language data, digitize available recordings and annotate these multimedia data in order to provide comprehensive language corpora as databases for future research on and for endangered – and under-described – Uralic speech communities. Applying language technology in language documentation helps us to create more systematically annotated corpora, rather than eclectic data collections. Specifically, we describe a script providing interactivity between different morphosyntactic analysis modules implemented as Finite State Transducers and ELAN, a Graphical User Interface tool for annotating and presenting multimodal corpora. Ultimately, the spoken corpora created in our projects will be useful for scientifically significant quantitative investigations on these languages in the future.

A North Saami to South Saami Machine Translation Prototype

The paper describes a rule-based machine translation (MT) system from North to South Saami. The system is designed for a workflow where North Saami functions as pivot language in translation from Norwegian or Swedish. We envisage manual translation from Norwegian or Swedish to North Saami, and thereafter MT to South Saami. The system was aimed at a single domain, that of texts for use in school administration. We evaluated the system in terms of the quality of translations for postediting. Two out of three of the Norwegian to South Saami professional translators found the output of the system to be useful. The evaluation shows that it is possible to make a functioning rule-based system with a small transfer lexicon and a small number of rules and achieve results that are useful for a restricted domain, even if there are substantial differences b etween the languages.

Low-Resource Active Learning of Morphological Segmentation

Many Uralic languages have a rich morphological structure, but lack morphological analysis tools needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications. We study how to create a statistical model for morphological segmentation with a large unannotated corpus and a small amount of annotated word forms selected using an active learning approach. We apply the procedure to two Finno-Ugric languages: Finnish and North Sámi. The semi-supervised Morfessor FlatCat method is used for statistical learning. For Finnish, we set up a simulated scenario to test various active learning query strategies. The best performance is provided by a coverage-based strategy on word initial and final substrings. For North Sámi we collect a set of humanannotated data. With 300 words annotated with our active learning setup, we see a relative improvement in morph boundary F1-score of 19% compared to unsupervised learning and 7.8% compared to random selection.