RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora

Dementieva, Daryna; Logacheva, Varvara; Nikishina, Irina; Fenogenova, Alena; Dale, David C.; Кротова, И. Н.; Semenov, Nikita; Shavrina, Tatiana; Panchenko, Alexander

doi:10.28995/2075-7182-2022-21-114-131

Cited by 6 publications

(3 citation statements)

References 13 publications

(17 reference statements)

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“…In a broader context, there have been works on speech strategies in interactions between interlocutors in situations of persuasion and provocation (Issers, 2009), functions of imperative verb forms in a situation of request or prompt (Paducheva, 2010), and extralinguistic conditions of spontaneous speech interactions (Zemskaja et al, 1981). Most of these studies describe qualitative traits of communication while quantitative studies have been confined to the tasks of automatic detection of speech aggression and detoxification of online communication (Dementieva et al, 2021;Dementieva et al, 2022). The aim of our project is to develop the annotation framework for oral communicative interaction that on one hand takes into account approaches from linguistic politeness theories and on the other could be used for qualitative research and the NLP applications.…”

Section: Introductionmentioning

confidence: 99%

Towards a Russian Multimedia Politeness Corpus

Klokova,

Krongauz,

Shulginov

et al. 2023

Computational Linguistics and Intellectual Technologies”

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Communication involves an exchange of information as well as the use of linguistic means to begin, sustain, and end conversations. Politeness is seen as one of the major language tools that facilitate smooth communication. In English, politeness has been an area of great interest in pragmatics, with various theories and corpus annotation approaches used to understand the relationship between politeness and social categories like power and gender, and to build Natural Language Processing applications. In Russian linguistics, politeness research has largely focused on lexical markers and speech strategies. This paper introduces the ongoing work on the development of the Russian Multimedia Politeness Corpus and discusses an annotation framework for oral communicative interaction, with an emphasis on adapting politeness theories for discourse annotation. The proposed approach lies in the identification of frames that encompass contextual information and the selection of relevant spatial, social, and relational features for the markup. The frames are then used to describe standard situations, which are marked by typical intentions and politeness formulae and paraverbal markers.

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Section: Introductionmentioning

confidence: 99%

Towards a Russian Multimedia Politeness Corpus

Klokova,

Krongauz,

Shulginov

et al. 2023

Computational Linguistics and Intellectual Technologies”

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“…The existing methods of text detoxification and style transfer are mostly made for the English language, which makes it difficult to transfer to other languages. For this purpose, the RUSSE Detoxification corpus (Dementieva et al, 2022) was developed to solve the detoxification problem in the Russian language. This paper describes the general problem statement and proposes a detoxification method based on RuT5 and describes in detail experiments with autoregressive (AR) and non-autoregressive models (NAR) for style transfer.…”

Section: Introductionmentioning

confidence: 99%

Between Denoising and Translation: Experiments in Text Detoxification

Pletenev¹

2022

Computational Linguistics and Intellectual Technologies

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This paper describes a solution for the RUSSE Detoxification competition held as part of the Dialogue 2022 conference. The paper presents experiments based on autoregressive and non-autoregressive models. The following approaches are described in this paper: 1) Detoxification as a special case of the text style-transfer problem and the use of modern approaches to solve this task in Russian. 2) Using the Automatic Post-Editing algorithm as a task of translation from toxic to normative Russian text. The article provides an analysis of the listed models, their results in detoxification of sentences, as well an analysis of errors and reasons why the models gave such a diverse result.

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“…That led to the domination of unsupervised approaches, such as . RUSSE Detox shared task (Dementieva et al, 2022) provides the first parallel detoxification dataset for Russian, which allows exploring the capabilities of generic text-to-text methods in application to the task. In this paper, we present a solution based on prompt tuning.…”

Section: Introductionmentioning

confidence: 99%

Prompt Tuning for Text Detoxification

Konodyuk¹

2022

Computational Linguistics and Intellectual Technologies

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Text detoxification is a challenging style transfer task, that implies paraphrasing into a neutral form while preserving the meaning as closely as possible. In this paper, we present a lightweight approach based on a recently proposed prompt tuning technique. Using RuGPT3-XL (Generative Pretrained Transformer-3 for Russian) as a frozen backbone, we train only a sequence of continuous embeddings inserted before and after an input text. Even though the number of trainable parameters is less than 0.025% of their total number, our approach achieves competitive performance compared to the methods involving full model tuning and ranks 4th on the leaderboard of shared RUSSE Detox task.

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RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora

Cited by 6 publications

References 13 publications

Towards a Russian Multimedia Politeness Corpus

Towards a Russian Multimedia Politeness Corpus

Between Denoising and Translation: Experiments in Text Detoxification

Prompt Tuning for Text Detoxification

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