Papers by Ekaterina Lapshinova-Koltunski

6 papers
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.

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