ymoslem/acl-abstracts huggingface dataset:
Towards standardized inflected lexicons for the Finnic languages (2024)

We introduce three richly annotated lexicons of nouns for Livonian, standard Finnish and Livvi Karelian. Our datasets are distributed in the machine-readable Paralex standard, which consists of linked CSV tables described in a JSON metadata file. We built on the morphological dictionary of Livonian, the VepKar database and the Omorfi software to provide inflected forms. All noun forms were transcribed with grapheme-to-phoneme conversion rules and the paradigms annotated for both overabundance and defectivity. The resulting datasets are usable for quantitative studies of morphological systems and for qualitative investigations. They are linked to the original resources and can be easily updated.
On Erzya and Moksha Corpora and Analyzer Development, ERME-PSLA 1950s (2024)

This paper describes materials and annotation facilitation pertinent to the {\guillemotleft}Erzya-Moksha Electronic Resources and Linguistic Diversity{\guillemotright} (EMERALD) project. It addresses work following the construction of finite-state analyzers for the Mordvin languages, the gathering of test corpora, and the development of metadata strategies for descriptive research. In this paper, we provide three descriptors for a set of new Erzya and Moksha research materials at the Language Bank of Finland. The descriptors illustrate (1) a low-annotation subcorpora set of the {\guillemotleft}Electronic Resources for Moksha and Erzya{\guillemotright} (ERME); (2) the state of the open-source analyzers used in their automatic annotation, and (3) the development of metadata documentation for the {\guillemotleft}EMERALD{\guillemotright} project, associated with this endeavor. Outcomes of the article include an introduction to new research materials, an illustration of the state of the Mordvin annotation pipeline, and perspectives for the further enhancement of the annotation pipeline.
Towards the speech recognition for Livonian (2024)

This article outlines the path toward the development of speech synthesis and speech recognition technologies for Livonian, a critically endangered Uralic language with around 20 contemporary fluent speakers. It presents the rationale behind the creation of these technologies and introduces the hypotheses and planned approaches to achieve this goal. The article discusses the four-stage approach of leveraging existing data and multiplying voice data through speech synthesis and voice cloning to generate the necessary data for building and training speech recognition for Livonian.
Using Large Language Models to Transliterate Endangered Uralic Languages (2024)

This study investigates whether the Large Language Models are able to transliterate and normalize endangered Uralic languages, specifically when they have been written in early 20th century Latin script based transcription systems. We test commercially available closed source systems where there is no reason to expect that the models would be particularly adjusted to this task or these languages. The output of the transliteration in all experiments is contemporary Cyrillic orthography. We conclude that some of the newer LLMs, especially Claude 3.5 Sonnet, are able to produce high quality transliterations even in the smaller languages in our test set, both in zero-shot scenarios and with a prompt that contains an example of the desired output. We assume that the good result is connected to the large presence of materials in these languages online, which the LLM has learned to represent.
Specialized Monolingual BPE Tokenizers for Uralic Languages Representation in Large Language Models (2024)

Large language models show significant inequality in language representation, particularly for Uralic languages. Our analysis found that existing tokenizers allocate minimal tokens to Uralic languages, highlighting this imbalance. To address this, we developed a pipeline to create clean monolingual datasets from Wikipedia articles for four Uralic languages. We trained Byte Pair Encoding (BPE) tokenizers with a vocabulary size of 256,000 tokens, though Northern Sami had only 93,187 due to limited data. Our findings revealed most tokens are unique to each language, with 8,102 shared across all four, and 25,876 shared among Estonian, Finnish, and Hungarian. Using the Compression Ratio metric, our tokenizers outperformed popular ones like LLaMA-2 and Gemma 2, reducing Finnish`s compression ratio from 3.41 to 1.18. These results demonstrate the importance of specialized tokenizers for underrepresented languages, improving model performance and lowering costs. By sharing our tokenizers and datasets, we provide crucial resources for further research, emphasizing the need for equitable language representation.
Compressing Noun Phrases to Discover Mental Constructions in Corpora -- A Case Study for Auxiliaries in Hungarian (2024)

The quantitative turn in functional linguistics has emphasised the importance of data-oriented methods in describing linguistic patterns. However, there are significant differences between constructions and the examples they cover, which need to be properly formalised. For example, noun chains introduce significant variation in the examples, making it difficult to identify underlying patterns. The compression of noun chains into their minimal form (e.g. as they appear in abstract constructions) is a promising method for revealing linguistic patterns in corpora through their examples. This method, combined with identifying the appropriate level of abstraction for the additional elements present, allows for the systematic extraction of good construction candidates. A pilot has been developed for Hungarian infinitive structures, but is adaptable for various linguistic structures and other agglutinative languages.
Prune or Retrain: Optimizing the Vocabulary of Multilingual Models for Estonian (2024)

Adapting multilingual language models to specific languages can enhance both their efficiency and performance. In this study, we explore how modifying the vocabulary of a multilingual encoder model to better suit the Estonian language affects its downstream performance on the Named Entity Recognition (NER) task. The motivations for adjusting the vocabulary are twofold: practical benefits affecting the computational cost, such as reducing the input sequence length and the model size, and performance enhancements by tailoring the vocabulary to the particular language. We evaluate the effectiveness of two vocabulary adaptation approaches{---}retraining the tokenizer and pruning unused tokens{---}and assess their impact on the model`s performance, particularly after continual training. While retraining the tokenizer degraded the performance of the NER task, suggesting that longer embedding tuning might be needed, we observed no negative effects on pruning.
On the Role of New Technologies in the Documentation and Revitalization of Uralic Languages of Russia in Historical and Contemporary Contexts (2024)

The Uralic languages spoken in Russia face significant challenges due to historical and sociopolitical factors, resulting in their endangered status. While only Finnish, Estonian, and Hungarian enjoy solid support as official languages, most Uralic languages suffer from limited resources and declining speaker populations. This paper examines the development of written Uralic languages, the impact of Russian language and its writing system to them, and the consequences of the lack of state interest in these languages for preservation efforts. Despite these challenges, technological advancements present valuable opportunities for revitalization. Existing projects, such as dictionaries and language corpora, highlight both the potential and shortcomings of current linguistic resources. Innovative approaches, including AI-based applications and user-driven platforms, can enhance engagement among people. By emphasizing the importance of high-quality linguistic data, this study advocates for a more proactive and collaborative effort in the preservation and promotion of Uralic languages.
Applying the transformer architecture on the task of headline selection for Finnish news texts (2024)

The paper evaluates the possibilities of using transformer architecture in creating headlines for news texts in Finnish. The authors statistically analyse the original and generated headlines according to three criteria: informativeness, relevance and impact. The study also substantiates for the first time the effectiveness of a fine-tuned text-to-text transfer transformer model within the task of generating headlines for news articles in Finnish. The results show that there is no statistically significant difference between the scores obtained by the original and generated headlines on the mentioned criteria of informativeness, relevance and impact.
Keeping Up Appearances---or how to get all Uralic languages included into bleeding edge research and software: generate, convert, and LLM your way into multilingual datasets (2024)

The current trends in natural language processing strongly favor large language models and generative AIs as the basis for everything. For Uralic languages that are not largely present in publically available data on the Internet, this can be problematic. In the current computational linguistic scene, it is very important to have representation of your language in popular datasets. Languages that are included in well-known datasets are also included in shared tasks, products by large technology corporations, and so forth. This inclusion will become especially important for under-resourced, under-studied minority, and Indigenous languages, which will otherwise be easily forgotten. In this article, we present the resources that are often deemed necessary for digital presence of a language in the large language model obsessed world of today. We show that there are methods and tricks available to alleviate the problems with a lack of data and a lack of creators and annotators of the data, some more successful than others.
Scaling Sustainable Development Goal Predictions across Languages: From English to Finnish (2024)

In this paper, we leverage an exclusive English dataset to train diverse multilingual classifiers, investigating their efficacy in adapting to Finnish data. We employ an exclusively English classification dataset of UN Sustainable Development Goals (SDG) in an education context, to train various multilingual classifiers and examine how well these models can adapt to recognizing the same classes within Finnish university course descriptions. It`s worth noting that Finnish, with a mere 5 million native speakers, presents a significantly less-resourced linguistic context compared to English. The best performing model in our experiments was mBART with an F1-score of 0.843.
Kola Saami Christian Text Corpus (2024)

Christian texts have been known to be printed in Kola Saami languages since 1828; the most extensive publication is the Gospel of Matthew, different translations of which have been published three times since 1878, most recently in 2022. The Lord`s Prayer was translated in several more versions in Kildin Saami and Skolt Saami, first in 1828. All of these texts seem to go back to translations from Rus- sian. Such characteristics make these pub- lications just right for parallel text align- ment. This paper describes ongoing work with building a Kola Saami Christian Text Cor- pus, including conceptional and technical decisions. Thus, it describes a resource, rather than a study. However, compu- tational studies based on these data will hopefully take place in the near future, af- ter the Kildin Saami subset of this corpus is finished and published by the end of 2024. In addition to computation, this resource will also allow for comparative linguistic studies on diachronic and synchronic vari- ation and change in Kola Saami languages, which are among the most endangered and least described Uralic languages.
The MEET Corpus: Collocated, Distant and Hybrid Three-party Meetings with a Ranking Task (2024)

We introduce the MEET corpus. The corpus was collected with the aim of systematically studying the effects of collocated (physical), remote (digital) and hybrid work meetings on collaborative decision-making. It consists of 10 sessions, where each session contains three recordings: a collocated, a remote and a hybrid meeting between three participants. The participants are working on a different survival ranking task during each meeting. The duration of each meeting ranges from 10 to 18 minutes, resulting in 380 minutes of conversation altogether. We also present the annotation scheme designed specifically to target our research questions. The recordings are currently being transcribed and annotated in accordance with this scheme
MSNER: A Multilingual Speech Dataset for Named Entity Recognition (2024)

While extensively explored in text-based tasks, Named Entity Recognition (NER) remains largely neglected in spoken language understanding. Existing resources are limited to a single, English-only dataset. This paper addresses this gap by introducing MSNER, a freely available, multilingual speech corpus annotated with named entities. It provides annotations to the VoxPopuli dataset in four languages (Dutch, French, German, and Spanish). We have also releasing an efficient annotation tool that leverages automatic pre-annotations for faster manual refinement. This results in 590 and 15 hours of silver-annotated speech for training and validation, alongside a 17-hour, manually-annotated evaluation set. We further provide an analysis comparing silver and gold annotations. Finally, we present baseline NER models to stimulate further research on this newly available dataset.
Attitudes in Diplomatic Speeches: Introducing the CoDipA UNSC 1.0 (2024)

This paper presents CoDipA UNSC 1.0, a Corpus of Diplomatic Attitudes of the United Nations Security Council annotated with the attitude-part of the Appraisal theory. The speeches were manually selected according to topic-related and temporal criteria. The texts were then annotated according to the predefined annotation scenario. The distinguishing features of the diplomatic texts require a modified approach to attitude evaluation, which was implemented and presented in the current work. The corpus analysis has proven diplomatic speeches to be consistently evaluative, offered an overview of the most prominent means of expressing subjectivity in the corpus, and provided the results of the inter-annotator agreement evaluation.
Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks (2024)

Existing discourse corpora are annotated based on different frameworks, which show significant dissimilarities in definitions of arguments and relations and structural constraints. Despite surface differences, these frameworks share basic understandings of discourse relations. The relationship between these frameworks has been an open research question, especially the correlation between relation inventories utilized in different frameworks. Better understanding of this question is helpful for integrating discourse theories and enabling interoperability of discourse corpora annotated under different frameworks. However, studies that explore correlations between discourse relation inventories are hindered by different criteria of discourse segmentation, and expert knowledge and manual examination are typically needed. Some semi-automatic methods have been proposed, but they rely on corpora annotated in multiple frameworks in parallel. In this paper, we introduce a fully automatic approach to address the challenges. Specifically, we extend the label-anchored contrastive learning method introduced by Zhang et al. (2022b) to learn label embeddings during discourse relation classification. These embeddings are then utilized to map discourse relations from different frameworks. We show experimental results on RST-DT (Carlson et al., 2001) and PDTB 3.0 (Prasad et al., 2018).
A New Annotation Scheme for the Semantics of Taste (2024)

This paper introduces a new annotation scheme for the semantics of gustatory language in English, which builds upon a previous framework for olfactory language based on frame semantics. The purpose of this annotation framework is to be used for annotating comparable resources for the study of sensory language and to create training datasets for supervised systems aimed at extracting sensory information. Furthermore, our approach incorporates words from specific historical periods, thereby enhancing the framework`s utility for studying language from a diachronic perspective.
What to Annotate: Retrieving Lexical Markers of Conspiracy Discourse from an Italian-English Corpus of Telegram Data (2024)

In this age of social media, Conspiracy Theories (CTs) have become an issue that can no longer be ignored. After providing an overview of CT literature and corpus studies, we describe the creation of a 40,000-token English-Italian bilingual corpus of conspiracy-oriented Telegram comments {--} the Complotto corpus {--} and the linguistic analysis we performed using the Sketch Engine online platform (Kilgarriff et al., 2010) on our annotated data to identify statistically relevant linguistic markers of CT discourse. Thanks to the platform`s keywords and key terms extraction functions, we were able to assess the statistical significance of the following lexical and semantic phenomena, both cross-linguistically and cross-CT, namely: (1) evidentiality and epistemic modality markers; (2) debunking vocabulary referring to another version of the truth lying behind the official one; (3) the conceptual metaphor INSTITUTIONS ARE ABUSERS. All these features qualify as markers of CT discourse and have the potential to be effectively used for future semantic annotation tasks to develop automatic systems for CT identification.
Lightweight Connective Detection Using Gradient Boosting (2024)

In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss.
Shallow Discourse Parsing on Twitter Conversations (2024)

We present our PDTB-style annotations on conversational Twitter data, which was initially annotated by Scheffler et al. (2019). We introduced 1,043 new annotations to the dataset, nearly doubling the number of previously annotated discourse relations. Subsequently, we applied a neural Shallow Discourse Parsing (SDP) model to the resulting corpus, improving its performance through retraining with in-domain data. The most substantial improvement was observed in the sense identification task (+19{\%}). Our experiments with diverse training data combinations underline the potential benefits of exploring various data combinations in domain adaptation efforts for SDP. To the best of our knowledge, this is the first application of Shallow Discourse Parsing on Twitter data
Search tool for An Event-Type Ontology (2024)

This short demo description paper presents a new tool designed for searching an event-type ontology with rich information, demonstrated on the SynSemClass ontology resource. The tool complements a web browser, created by the authors of the SynSemClass ontology previously. Due to the complexity of the resource, the search tool offers possibilities both for a linguistically-oriented researcher as well as for teams working with the resource from a technical point of view, such as building role labeling tools, automatic annotation tools, etc.
Tiny But Mighty: A Crowdsourced Benchmark Dataset for Triple Extraction from Unstructured Text (2024)

In the context of Natural Language Processing (NLP) and Semantic Web applications, constructing Knowledge Graphs (KGs) from unstructured text plays a vital role. Several techniques have been developed for KG construction from text, but the lack of standardized datasets hinders the evaluation of triple extraction methods. The evaluation of existing KG construction approaches is based on structured data or manual investigations. To overcome this limitation, this work introduces a novel dataset specifically designed to evaluate KG construction techniques from unstructured text. Our dataset consists of a diverse collection of compound and complex sentences meticulously annotated by human annotators with potential triples (subject, verb, object). The annotations underwent further scrutiny by expert ontologists to ensure accuracy and consistency. For evaluation purposes, the proposed F-measure criterion offers a robust approach to quantify the relatedness and assess the alignment between extracted triples and the ground-truth triples, providing a valuable tool for evaluating the performance of triple extraction systems. By providing a diverse collection of high-quality triples, our proposed benchmark dataset offers a comprehensive training and evaluation set for refining the performance of state-of-the-art language models on a triple extraction task. Furthermore, this dataset encompasses various KG-related tasks, such as named entity recognition, relation extraction, and entity linking.
Less is Enough: Less-Resourced Multilingual AMR Parsing (2024)

This paper investigates the efficacy of multilingual models for the task of text-to-AMR parsing, focusing on English, Spanish, and Dutch. We train and evaluate models under various configurations, including monolingual and multilingual settings, both in full and reduced data scenarios. Our empirical results reveal that while monolingual models exhibit superior performance, multilingual models are competitive across all languages, offering a more resource-efficient alternative for training and deployment. Crucially, our findings demonstrate that AMR parsing benefits from transfer learning across languages even when having access to significantly smaller datasets. As a tangible contribution, we provide text-to-AMR parsing models for the aforementioned languages as well as multilingual variants, and make available the large corpora of translated data for Dutch, Spanish (and Irish) that we used for training them in order to foster AMR research in non-English languages. Additionally, we open-source the training code and offer an interactive interface for parsing AMR graphs from text.
MoCCA: A Model of Comparative Concepts for Aligning Constructicons (2024)

This paper presents MoCCA, a Model of Comparative Concepts for Aligning Constructicons under development by a consortium of research groups building Constructicons of different languages including Brazilian Portuguese, English, German and Swedish. The Constructicons will be aligned by using comparative concepts (CCs) providing language-neutral definitions of linguistic properties. The CCs are drawn from typological research on grammatical categories and constructions, and from FrameNet frames, organized in a conceptual network. Language-specific constructions are linked to the CCs in accordance with general principles. MoCCA is organized into files of two types: a largely static CC Database file and multiple Linking files containing relations between constructions in a Constructicon and the CCs. Tools are planned to facilitate visualization of the CC network and linking of constructions to the CCs. All files and guidelines will be versioned, and a mechanism is set up to report cases where a language-specific construction cannot be easily linked to existing CCs.