Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, Peteris Paikens
| Challenge: | Using full stack of language resources, we are creating a balanced text corpus for Latvian. |
| Approach: | They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus . |
| Outcome: | The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian. |
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Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020.lrec-1)
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| Challenge: | a paper aims at enriching the manually annotated part of National Corpus of Polish with a syntactic layer. |
| Approach: | They enrich manually annotated part of Polish National Corpus with a syntactic layer and a UD dependency graph. |
| Outcome: | The proposed model outperforms a model trained on a smaller set of gold-standard trees in predicting part-of-speech tags, morphological features, lemmata and labelled dependency trees. |
MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus (2024.naacl-long)
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Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli
| Challenge: | Existing approaches to integrate semantics into Natural Language Understanding (NLP) systems are cost-effective and environmental impact-related. |
| Approach: | They propose to provide semantically-annotated corpora for four NLU tasks across five languages and to drop the requirement of closed datasets. |
| Outcome: | The proposed model provides hundreds of millions of silver yet high-quality annotations for four NLU tasks across five languages. |
BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages (2024.lrec-main)
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Roberts Dargis, Arturs Znotins, Ilze Auzina, Baiba Saulite, Sanita Reinsone, Raivis Dejus, Antra Klavinska, Normunds Gruzitis
| Challenge: | Latvian is a low-resource language for many NLP tasks, but most speech corpora are closed data . a crowdsourcing campaign to create a relatively large, diverse and open speech corpus for Latvian has been launched . |
| Approach: | a crowdsourcing campaign is helping to create an open speech corpus for Latvian . the goal is to enlarge the datasets and make them more diverse . authors use the opensource Mozilla Common Voice platform to validate speech samples . |
| Outcome: | a crowdsourcing initiative has increased the size and speaker diversity of the Latvian Common Voice 17.0 dataset by more than tenfold in less than a year. |
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue (2022.findings-acl)
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| Challenge: | Multi-SentAugment and LayerAgg are self-training methods that augment available training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora. |
| Approach: | They propose to use multi-sentaugment and layeragg to improve dialogue natural language understanding across multiple languages. |
| Outcome: | The proposed methods generalise well in zero- and few-shot scenarios and leverage external unannotated data sources. |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
AMR Beyond the Sentence: the Multi-sentence AMR corpus (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences. |
| Approach: | They propose a corpus that annotates coreference and similar phenomena on top of existing AMRs. |
| Outcome: | The proposed corpus is compared with existing corpora on sentence-level semantics . it shows that it can be used for information extraction and question answering . |
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)
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Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, Daniel Zeman
| Challenge: | Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages. |
| Approach: | They describe version 2 of the universal guidelines and discuss major changes from UD v1 to UD 2 . they propose a morphological layer, a syntactic layer and a word segmentation layer . |
| Outcome: | The proposed treebanks are available for 90 languages and have been updated to meet the needs of multilingual parsers and researchers. |
Cross-lingual Semantic Representation for NLP with UCCA (2020.coling-tutorials)
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| Challenge: | introductory tutorial to UCCA, a symbolic meaning representation for semantic representations. |
| Approach: | This tutorial introduces UCCA, a cross-linguistically applicable framework for semantic representation . it will provide a detailed introduction to the UCca annotation guidelines, design philosophy and available resources . |
| Outcome: | The tutorial will provide a detailed introduction to the UCCA framework and compare it to other meaning representations. |
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area. |
| Approach: | They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach. |
| Outcome: | The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues. |
EmpiriST Corpus 2.0: Adding Manual Normalization, Lemmatization and Semantic Tagging to a German Web and CMC Corpus (2020.lrec-1)
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| Challenge: | EmpiriST corpus is a manually tokenized and part-of-speech tagged corpus of German web and CMC data. |
| Approach: | They extend the corpus with manually tokenized annotation layers for word form normalization, lemmatization and lexical semantics. |
| Outcome: | The EmpiriST corpus is a manually tokenized and part-of-speech tagged corpus of German web and CMC data. |