Papers by Ekaterina Lapshinova-Koltunski
ParCorFull: a Parallel Corpus Annotated with Full Coreference (L18-1)
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| Challenge: | Recent research in multilingual coreference and automatic pronoun translation has led to important insights into the problem and some promising results. |
| Approach: | They propose a corpus annotated with full coreference chains that addresses a problem that machine translation and other multilingual natural language processing (NLP) technologies face: translation of coreference across languages. |
| Outcome: | The proposed corpus contains parallel texts for the language pair English-German, two major European languages. |
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
| Outcome: | The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications. |
EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus (2022.lrec-1)
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| Challenge: | EPIC UdS is a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Approach: | They describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Outcome: | The proposed corpus includes transcripts suitable for research on more than one language pair and on interpreting with regard to German. |
Analysing Coreference in Transformer Outputs (D19-65)
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| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
| Approach: | They analyse coreference phenomena in three neural machine translation systems . they manually annotate (the possibly incorrect) coreference chains in the outputs . |
| Outcome: | The proposed model shows stronger translationese effects in machine translated outputs than in human translations. |
Lexicogrammatic translationese across two targets and competence levels (2020.lrec-1)
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| Challenge: | a specificity of translations with English as a source language produced by students and professional translators is investigated by genre-comparable data from a number of parallel and comparable corpora. |
| Approach: | They propose to use genre-comparable data to explore the specificity of translations . they use a set of human-interpretable lexicogrammatic translationese indicators . |
| Outcome: | The proposed feature set can reliably distinguish translations and non-translations regardless of the language pair and translation variety. |
DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations (2022.lrec-1)
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| Challenge: | a new corpus of human translations contains both professional and student translations of news and reviews texts. |
| Approach: | They propose to use the data to compare human and professional translations of news and reviews in a new corpus which contains both professional and student translations. |
| Outcome: | The proposed corpus contains professional and student translations of news and reviews and a subcorpus containing reviews into Finnish. |