Search Results for author: Varvara Logacheva

Found 25 papers, 6 papers with code

Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation

1 code implementation9 Mar 2021 Nikolay Babakov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko

We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness.

RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language

no code implementations22 May 2020 Irina Nikishina, Varvara Logacheva, Alexander Panchenko, Natalia Loukachevitch

This paper describes the results of the first shared task on taxonomy enrichment for the Russian language.

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

no code implementations LREC 2020 Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko

We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages.

Word Embeddings Word Sense Disambiguation

MIPT System for World-Level Quality Estimation

no code implementations WS 2019 Mikhail Mosyagin, Varvara Logacheva

We explore different model architectures for the WMT 19 shared task on word-level quality estimation of automatic translation.

Findings of the WMT 2018 Shared Task on Quality Estimation

no code implementations WS 2018 Lucia Specia, Fr{\'e}d{\'e}ric Blain, Varvara Logacheva, Ram{\'o}n Astudillo, Andr{\'e} F. T. Martins

We report the results of the WMT18 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document.

Machine Translation

Phrase Level Segmentation and Labelling of Machine Translation Errors

no code implementations LREC 2016 Fr{\'e}d{\'e}ric Blain, Varvara Logacheva, Lucia Specia

This paper presents our work towards a novel approach for Quality Estimation (QE) of machine translation based on sequences of adjacent words, the so-called phrases.

Machine Translation

A Quality-based Active Sample Selection Strategy for Statistical Machine Translation

no code implementations LREC 2014 Varvara Logacheva, Lucia Specia

Our approach is based on a quality estimation technique which involves a wider range of features of the source text, automatic translation, and machine translation system compared to previous work.

Active Learning Machine Translation +1

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