Anonymisation, that is identifying and neutralising sensitive references, is a crucial part of dataset creation. In this paper, we describe the anonymisation process of a Turkish-German code-switching corpus, namely SAGT, which consists of speech data and a treebank that is built on its transcripts. We employed a selective pseudonymisation approach where we manually identified sensitive references to anonymise and replaced them with surrogate values on the treebank side. In addition to maintaining data privacy, our primary concerns in surrogate selection were keeping the integrity of code-switching properties, morphosyntactic annotation layers, and semantics. After the treebank anonymisation, we anonymised the speech data by mapping between the treebank sentences and audio transcripts with the help of Praat scripts. The treebank is publicly available for research purposes and the audio files can be obtained via an individual licence agreement.

Lexical normalization, the translation of noncanonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media.Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media.In this paper, we propose three normalization models specifically designed to handle codeswitched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De).For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging.Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input. 1

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.We compare model performance in a simulated low-resource setting for the highresource languages German, English, and Indonesian to experiments on new datasets for the truly low-resource languages Popoluca and Tepehua.We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning.We find that, in the low-resource setting, the novel approaches outperform existing ones on all languages by up to 11.4% accuracy.However, while accuracy in emulated low-resource scenarios is over 50% for all languages, for the truly lowresource languages Popoluca and Tepehua, our best model only obtains 37.4% and 28.4% accuracy, respectively.Thus, we conclude that canonical segmentation is still a challenging task for low-resource languages.

Code-switching has become a prevalent phenomenon across many communities. It poses a challenge to NLP researchers, mainly due to the lack of available data needed for training and testing applications. In this paper, we introduce a new resource: a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags. Beside the corpus itself, we provide annotation guidelines to address the unique challenges of annotating code-switch data. Another challenge that we address is the fact that Egyptian Arabic orthography and grammar are not standardized.

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic structures relies on correct segmentation. We present a graph-based lattice dependency parser that operates on morphological lattices to represent different segmentations and morphological analyses for a given input sentence. The lattice parser predicts a dependency tree over a path in the lattice and thus solves the joint task of segmentation, morphological analysis, and syntactic parsing. We conduct experiments on the Turkish and the Hebrew treebank and show that the joint model outperforms three state-of-the-art pipeline systems on both data sets. Our work corroborates findings from constituency lattice parsing for Hebrew and presents the first results for full lattice parsing on Turkish.

This paper investigates the use of sublexical units as a solution to handling the complex morphology with productive derivational processes, in the development of a lexical functional grammar for Turkish.Such sublexical units make it possible to expose the internal structure of words with multiple derivations to the grammar rules in a uniform manner.This in turn leads to more succinct and manageable rules.Further, the semantics of the derivations can also be systematically reflected in a compositional way by constructing PRED values on the fly.We illustrate how we use sublexical units for handling simple productive derivational morphology and more interesting cases such as causativization, etc., which change verb valency.Our priority is to handle several linguistic phenomena in order to observe the effects of our approach on both the c-structure and the f-structure representation, and grammar writing, leaving the coverage and evaluation issues aside for the moment.