Alexey Tikhonov


2022

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EENLP: Cross-lingual Eastern European NLP Index
Alexey Tikhonov | Alex Malkhasov | Andrey Manoshin | George-Andrei Dima | Réka Cserháti | Md.Sadek Hossain Asif | Matt Sárdi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Motivated by the sparsity of NLP resources for Eastern European languages, we present a broad index of existing Eastern European language resources (90+ datasets and 45+ models) published as a github repository open for updates from the community. Furthermore, to support the evaluation of commonsense reasoning tasks, we provide hand-crafted cross-lingual datasets for five different semantic tasks (namely news categorization, paraphrase detection, Natural Language Inference (NLI) task, tweet sentiment detection, and news sentiment detection) for some of the Eastern European languages. We perform several experiments with the existing multilingual models on these datasets to define the performance baselines and compare them to the existing results for other languages.

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HeadlineCause: A Dataset of News Headlines for Detecting Causalities
Ilya Gusev | Alexey Tikhonov
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. We also present a set of models and experiments that demonstrates the dataset validity, including a multilingual XLM-RoBERTa based model for causality detection and a GPT-2 based model for possible effects prediction.

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Transformers in the loop: Polarity in neural models of language
Lisa Bylinina | Alexey Tikhonov
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Representation of linguistic phenomena in computational language models is typically assessed against the predictions of existing linguistic theories of these phenomena. Using the notion of polarity as a case study, we show that this is not always the most adequate set-up. We probe polarity via so-called ‘negative polarity items’ (in particular, English ‘any’) in two pre-trained Transformer-based models (BERT and GPT-2). We show that – at least for polarity – metrics derived from language models are more consistent with data from psycholinguistic experiments than linguistic theory predictions. Establishing this allows us to more adequately evaluate the performance of language models and also to use language models to discover new insights into natural language grammar beyond existing linguistic theories. This work contributes to establishing closer ties between psycholinguistic experiments and experiments with language models.

2021

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StoryDB: Broad Multi-language Narrative Dataset
Alexey Tikhonov | Igor Samenko | Ivan Yamshchikov
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

This paper presents StoryDB — a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories. Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones. We also demonstrate how the dataset could be used to benchmark three modern multilanguage models, namely, mDistillBERT, mBERT, and XLM-RoBERTa.

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It’s All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning
Alexey Tikhonov | Max Ryabinin
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2019

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Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
Alexey Tikhonov | Viacheslav Shibaev | Aleksander Nagaev | Aigul Nugmanova | Ivan P. Yamshchikov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.

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Decomposing Textual Information For Style Transfer
Ivan P. Yamshchikov | Viacheslav Shibaev | Aleksander Nagaev | Jürgen Jost | Alexey Tikhonov
Proceedings of the 3rd Workshop on Neural Generation and Translation

This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.

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Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings
Ivan P. Yamshchikov | Viascheslav Shibaev | Alexey Tikhonov
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.