15 research outputs found
Digitising Swiss German : how to process and study a polycentric spoken language
Swiss dialects of German are, unlike many dialects of other standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety and that it is subject to considerable regional variation. This paper presents the ArchiMob corpus, a freely available general-purpose corpus of spoken Swiss German based on oral history interviews. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first present the modalities of access of the corpus for linguistic, historic and computational research. We then describe how the documents were transcribed, segmented and aligned with the sound source. This work involved a series of experiments that have led to automatically annotated normalisation and part-of-speech tagging layers. Finally, we present several case studies to motivate the use of the corpus for digital humanities in general and for dialectology in particular.Peer reviewe
ArchiMob : A multidialectal corpus of Swiss German spontaneous speech
Alemannische Dialektologie – Forschungsstand und Perspektiven. SonderheftPeer reviewe
SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German
Swiss German is a dialect continuum whose natively acquired dialects
significantly differ from the formal variety of the language. These dialects
are mostly used for verbal communication and do not have standard orthography.
This has led to a lack of annotated datasets, rendering the use of many NLP
methods infeasible. In this paper, we introduce the first annotated parallel
corpus of spoken Swiss German across 8 major dialects, plus a Standard German
reference. Our goal has been to create and to make available a basic dataset
for employing data-driven NLP applications in Swiss German. We present our data
collection procedure in detail and validate the quality of our corpus by
conducting experiments with the recent neural models for speech synthesis
A Large-Scale Comparison of Historical Text Normalization Systems
There is no consensus on the state-of-the-art approach to historical text
normalization. Many techniques have been proposed, including rule-based
methods, distance metrics, character-based statistical machine translation, and
neural encoder--decoder models, but studies have used different datasets,
different evaluation methods, and have come to different conclusions. This
paper presents the largest study of historical text normalization done so far.
We critically survey the existing literature and report experiments on eight
languages, comparing systems spanning all categories of proposed normalization
techniques, analysing the effect of training data quantity, and using different
evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201
Natural language processing for similar languages, varieties, and dialects: A survey
There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.Non peer reviewe
A Report on the Third VarDial Evaluation Campaign
Non peer reviewe
New Developments in Tagging Pre-modern Orthodox Slavic Texts
Pre-modern Orthodox Slavic texts pose certain difficulties when it comes to part-of-speech and full morphological tagging. Orthographic and morphological heterogeneity makes it hard to apply resources that rely on normalized data, which is why previous attempts to train part-of-speech (POS) taggers for pre-modern Slavic often apply normalization routines. In the current paper, we further explore the normalization path; at the same time, we use the statistical CRF-tagger MarMoT and a newly developed neural network tagger that cope better with variation than previously applied rule-based or statistical taggers. Furthermore, we conduct transfer experiments to apply Modern Russian resources to pre-modern data. Our experiments show that while transfer experiments could not improve tagging performance significantly, state-of-the-art taggers reach between 90% and more than 95% tagging accuracy and thus approach the tagging accuracy of modern standard languages with rich morphology. Remarkably, these results are achieved without the need for normalization, which makes our research of practical relevance to the Paleoslavistic community.Peer reviewe