Speech emotion recognition is in the focus of research since several decades and has many applications. One problem is sparse data for supervised learning. One way to tackle this problem is the synthesis of data with emotion simulating speech synthesis approaches. We present a synthesized database of five basic emotions and neutral expression based on rule based manipulation for a diphone synthesizer which we release to the public. The database has been validated in several machine learning experiments as a training set to detect emotional expression from natural speech data. The scripts to generate such a database have been made open source and could be used to aid speech emotion recognition for a low resourced language, as MBROLA supports 35 languages

Research has shown the potential negative impact of social media usage on body image. Various platforms present numerous medial formats of possibly harmful content related to eating disorders. Different cultural backgrounds, represented, for example, by different languages, are participating in the discussion online. Therefore, this research aims to investigate eating disorder specific content in a multilingual and multimedia environment. We want to contribute to establishing a common ground for further automated approaches. Our first objective is to combine the two media formats, text and image, by classifying the posts from one social media platform (Reddit) and continuing the categorization in the second (Tumblr). Our second objective is the analysis of multilingualism. We worked qualitatively in an iterative valid categorization process, followed by a comparison of the portrayal of eating disorders on both platforms. Our final data sets contained 960 Reddit and 2 081 Tumblr posts. Our analysis revealed that Reddit users predominantly exchange content regarding disease and eating behaviour, while on Tumblr, the focus is on the portrayal of oneself and one’s body.

In Japanese, there are different expressions used in speech depending on the speaker’s and listener’s social status, called honorifics. Unlike other languages, Japanese has many types of honorific expressions, and it is vital for machine translation and dialogue systems to handle the differences in meaning correctly. However, there is still no corpus that deals with honorific expressions based on social status. In this study, we developed an honorific corpus (KeiCO corpus) that includes social status information based on Systemic Functional Linguistics, which expresses language use in situations from the social group’s values and common understanding. As a general-purpose language resource, it filled in the Japanese honorific blanks. We expect the KeiCO corpus could be helpful for various tasks, such as improving the accuracy of machine translation, automatic evaluation, correction of Japanese composition and style transformation. We also verified the accuracy of our corpus by a BERT-based classification task.

In this paper, we present the first Entity Linking corpus for Icelandic. We describe our approach of using a multilingual entity linking model (mGENRE) in combination with Wikipedia API Search (WAPIS) to label our data and compare it to an approach using WAPIS only. We find that our combined method reaches 53.9% coverage on our corpus, compared to 30.9% using only WAPIS. We analyze our results and explain the value of using a multilingual system when working with Icelandic. Additionally, we analyze the data that remain unlabeled, identify patterns and discuss why they may be more difficult to annotate.

The “Web as corpus” paradigm opens opportunities for enhancing the current state of language resources for endangered and under-resourced languages. However, standard crawling strategies tend to overlook available resources of these languages in favor of already well-documented ones. Since 2016, the “Crawling Under-Resourced Languages” portal (CURL) has been contributing to bridging the gap between established crawling techniques and knowledge about relevant Web resources that is only available in the specific language communities. The aim of the CURL portal is to enlarge the amount of available text material for under-resourced languages thereby developing available datasets further and to use them as a basis for statistical evaluation and enrichment of already available resources. The application is currently provided and further developed as part of the thematic cluster “Non-Latin scripts and Under-resourced languages” in the German national research consortium Text+. In this context, its focus lies on the extraction of text material and statistical information for the data domain “Lexical resources”.

In this paper, we present a number of fine-grained resources for Natural Language Inference (NLI). In particular, we present a number of resources and validation methods for Greek NLI and a resource for precise NLI. First, we extend the Greek version of the FraCaS test suite to include examples where the inference is directly linked to the syntactic/morphological properties of Greek. The new resource contains an additional 428 examples, making it in total a dataset of 774 examples. Expert annotators have been used in order to create the additional resource, while extensive validation of the original Greek version of the FraCaS by non-expert and expert subjects is performed. Next, we continue the work initiated by (CITATION), according to which a subset of the RTE problems have been labeled for missing hypotheses and we present a dataset an order of magnitude larger, annotating the whole SuperGlUE/RTE dataset with missing hypotheses. Lastly, we provide a de-dropped version of the Greek XNLI dataset, where the pronouns that are missing due to the pro-drop nature of the language are inserted. We then run some models to see the effect of that insertion and report the results.

This paper discusses the compilation of the words.hk Cantonese dictionary dataset, which was compiled through manual annotation over a period of 7 years. Cantonese is a low-resource language with limited tagged or manually checked resources, especially at the sentential level, and this dataset is an attempt to fill the gap. The dataset contains over 53,000 entries of Cantonese words, which comes with basic lexical information (Jyutping phonemic transcription, part-of-speech tags, usage tags), manually crafted definitions in Written Cantonese, English translations, and Cantonese examples with English translation and Jyutping transliterations. Special attention has been paid to handle character variants, so that unintended “character errors” (equivalent to typos in phonemic writing systems) are filtered out, and intra-speaker variants are handled. Fine details on word segmentation, character variant handling, definition crafting will be discussed. The dataset can be used in a wide range of natural language processing tasks, such as word segmentation, construction of semantic web and training of models for Cantonese transliteration.

In recent years there has been great interest in addressing the data scarcity of African languages and providing baseline models for different Natural Language Processing tasks (Orife et al., 2020). Several initiatives (Nekoto et al., 2020) on the continent uses the Bible as a data source to provide proof of concept for some NLP tasks. In this work, we present the Lingala Speech Translation (LiSTra) dataset, release a full pipeline for the construction of such dataset in other languages, and report baselines using both the traditional cascade approach (Automatic Speech Recognition - Machine Translation), and a revolutionary transformer based End-2-End architecture (Liu et al., 2020) with a custom interactive attention that allows information sharing between the recognition decoder and the translation decoder.

In this paper, an approach for hate speech detection against women in the Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic_fr_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performa

This paper reports work on building a word-level language identification (LID) model for code-mixed Bangla-English social media data using subword embeddings, with an ultimate goal of using this LID module as the first step in a modular part-of-speech (POS) tagger in future research. This work reports preliminary results of a word-level LID model that uses a single bidirectional LSTM with subword embeddings trained on very limited code-mixed resources. At the time of writing, there are no previous reported results available in which subword embeddings are used for language identification with the Bangla-English code-mixed language pair. As part of the current work, a labeled resource for word-level language identification is also presented, by correcting 85.7% of labels from the 2016 ICON Whatsapp Bangla-English dataset. The trained model was evaluated on a test set of 4,015 tokens compiled from the 2015 and 2016 ICON datasets, and achieved a test accuracy of 93.61%.