Search Results for author: Irina Krotova

Found 6 papers, 4 papers with code

ParaDetox: Detoxification with Parallel Data

1 code implementation ACL 2022 Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, Daniil Moskovskiy, David Dale, Irina Krotova, Nikita Semenov, Alexander Panchenko

To the best of our knowledge, these are the first parallel datasets for this task. We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources. We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches.

Sentence

RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification

1 code implementation LREC 2022 Nikita Martynov, Irina Krotova, Varvara Logacheva, Alexander Panchenko, Olga Kozlova, Nikita Semenov

We compare it to the largest available dataset for Russian ParaPhraser and show that the best available paraphrase identifiers for the Russian language fail on the RuPAWS dataset.

Paraphrase Identification

Don't lose the message while paraphrasing: A study on content preserving style transfer

1 code implementation17 Aug 2023 Nikolay Babakov, David Dale, Ilya Gusev, Irina Krotova, Alexander Panchenko

Text style transfer techniques are gaining popularity in natural language processing allowing paraphrasing text in the required form: from toxic to neural, from formal to informal, from old to the modern English language, etc.

Style Transfer Text Style Transfer

Studying the role of named entities for content preservation in text style transfer

2 code implementations20 Jun 2022 Nikolay Babakov, David Dale, Varvara Logacheva, Irina Krotova, Alexander Panchenko

Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer.

Style Transfer Text Style Transfer

A Joint Approach to Compound Splitting and Idiomatic Compound Detection

no code implementations LREC 2020 Irina Krotova, Sergey Aksenov, Ekaterina Artemova

Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out-of-vocabulary (OOV) words.

Information Retrieval Machine Translation +4

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