Olga Kozlova


2021

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Text Detoxification using Large Pre-trained Neural Models
David Dale | Anton Voronov | Daryna Dementieva | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.

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Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation
Nikolay Babakov | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

Not all topics are equally “flammable” in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.

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Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Artem Shelmanov | Dmitri Puzyrev | Lyubov Kupriyanova | Denis Belyakov | Daniil Larionov | Nikita Khromov | Olga Kozlova | Ekaterina Artemova | Dmitry V. Dylov | Alexander Panchenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice.

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SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection
David Dale | Igor Markov | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a binary sequence tagging problem. We show that fine-tuning a RoBERTa model for this problem is a strong baseline. This baseline can be further improved by pre-training the RoBERTa model on a large dataset labeled for toxicity at the sentence level. While our solution scored among the top 20% participating models, it is only 2 points below the best result. This suggests the viability of our approach.