Papers with Language Technology
Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)
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| Challenge: | Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese. |
| Approach: | They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties. |
| Outcome: | The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style. |
Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies (2020.lrec-1)
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| Challenge: | Icelandic language is low-resource and is not yet considered in imminent danger . efforts underway to make it accessible and usable in Language Technology . |
| Approach: | They propose to build a parallel Icelandic dependency treebank based on Universal Dependencies (UD) this is the first parallel treebank resource for the language and several other languages already have one . |
| Outcome: | The proposed treebank is the first parallel treebank resource for the low-resource language . the project will be published as part of UD version 2.6. |
A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing have lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. |
| Approach: | They propose to annotate court transcripts from genocide-related cases using transformer-based approaches and to establish benchmarks for the task of paragraph identification of violence-related witness statements. |
| Outcome: | The first annotated corpus of genocide-related court transcripts is aimed at providing a first reference corpus for the community and to establish benchmark performances using state-of-the-art transformer-based approaches. |