Linking discourse-level information and the induction of bilingual discourse connective lexicons

Özer, Sibel; Kurfalı, Murathan; Zeyrek, Deniz; Mendes, Amália; Oleškevičienė, Giedrė Valūnaitė

doi:10.3233/sw-223011

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Instead, discourse-related data continues to be published in resource-, domain-or community-specific formats (e.g. [191]).…”

Section: Pragmaticsmentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

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Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

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“…Instead, discourse-related data continues to be published in resource-, domain-or community-specific formats (e.g. [191]).…”

Section: Pragmaticsmentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

View full text Add to dashboard Cite

show abstract

Toward a shallow discourse parser for Turkish

2023

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One of the most interesting aspects of natural language is how texts cohere, which involves the pragmatic or semantic relations that hold between clauses (addition, cause-effect, conditional, similarity), referred to as discourse relations. A focus on the identification and classification of discourse relations appears as an imperative challenge to be resolved to support tasks such as text summarization, dialogue systems, and machine translation that need information above the clause level. Despite the recent interest in discourse relations in well-known languages such as English, data and experiments are still needed for typologically different and less-resourced languages. We report the most comprehensive investigation of shallow discourse parsing in Turkish, focusing on two main sub-tasks: identification of discourse relation realization types and the sense classification of explicit and implicit relations. The work is based on the approach of fine-tuning a pre-trained language model (BERT) as an encoder and classifying the encoded data with neural network-based classifiers. We firstly identify the discourse relation realization type that holds in a given text, if there is any. Then, we move on to the sense classification of the identified explicit and implicit relations. In addition to in-domain experiments on a held-out test set from the Turkish Discourse Bank (TDB 1.2), we also report the out-domain performance of our models in order to evaluate its generalization abilities, using the Turkish part of the TED Multilingual Discourse Bank. Finally, we explore the effect of multilingual data aggregation on the classification of relation realization type through a cross-lingual experiment. The results suggest that our models perform relatively well despite the limited size of the TDB 1.2 and that there are language-specific aspects of detecting the types of discourse relation realization. We believe that the findings are important both in providing insights regarding the performance of the modern language models in a typologically different language and in the low-resource scenario, given that the TDB 1.2 is 1/20th of the Penn Discourse TreeBank in terms of the number of total relations.

show abstract

Linking discourse-level information and the induction of bilingual discourse connective lexicons

Cited by 2 publications

References 30 publications

Multilinguality and LLOD: A survey across linguistic description levels

Multilinguality and LLOD: A survey across linguistic description levels

Toward a shallow discourse parser for Turkish

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