Irina Krotova


2022

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ParaDetox: Detoxification with Parallel Data
Varvara Logacheva | Daryna Dementieva | Sergey Ustyantsev | Daniil Moskovskiy | David Dale | Irina Krotova | Nikita Semenov | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. 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. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.

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A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification
Varvara Logacheva | Daryna Dementieva | Irina Krotova | Alena Fenogenova | Irina Nikishina | Tatiana Shavrina | Alexander Panchenko
Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)

It is often difficult to reliably evaluate models which generate text. Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters.We conduct an evaluation of a large number of models on a detoxification task. We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text. Automatic metrics tend to be less reliable for better-performing models. However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.

2020

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A Joint Approach to Compound Splitting and Idiomatic Compound Detection
Irina Krotova | Sergey Aksenov | Ekaterina Artemova
Proceedings of the 12th Language Resources and Evaluation Conference

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 words. In-depth processing of noun compounds requires not only splitting them into smaller components (or even roots) but also the identification of instances that should remain unsplitted as they are of idiomatic nature. We develop a two-fold deep learning-based approach of noun compound splitting and idiomatic compound detection for the German language that we train using a newly collected corpus of annotated German compounds. Our neural noun compound splitter operates on a sub-word level and outperforms the current state of the art by about 5%