32 citations · 212 across the 25 of their papers we have counts for
27 papers
Knowledge Graph Representation for Political Information Sources
Tinatin Osmonova, Alexey Tikhonov, Ivan P. Yamshchikov
With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessibl…
Post Turing: Mapping the landscape of LLM Evaluation
Alexey Tikhonov, Ivan P. Yamshchikov
In the rapidly evolving landscape of Large Language Models (LLMs), introduction of well-defined and standardized evaluation methodologies remains a crucial challenge. This paper tr…
What is Wrong with Language Models that Can Not Tell a Story?
Ivan P. Yamshchikov, Alexey Tikhonov
This paper argues that a deeper understanding of narrative and the successful generation of longer subjectively interesting texts is a vital bottleneck that hinders the progress in…
Good Classification Measures and How to Find Them
Martijn Gösgens, Anton Zhiyanov, Alexey Tikhonov +1
Several performance measures can be used for evaluating classification results: accuracy, F-measure, and many others. Can we say that some of them are better than others, or, ideal…
Fine-Tuning Transformers: Vocabulary Transfer
Vladislav Mosin, Igor Samenko, Alexey Tikhonov +2
Transformers are responsible for the vast majority of recent advances in natural language processing. The majority of practical natural language processing applications of these mo…
StoryDB: Broad Multi-language Narrative Dataset
Alexey Tikhonov, Igor Samenko, Ivan P. Yamshchikov
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 50…
HeadlineCause: A Dataset of News Headlines for Detecting Causalities
Ilya Gusev, Alexey Tikhonov
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…
Actionable Entities Recognition Benchmark for Interactive Fiction
Alexey Tikhonov, Ivan P. Yamshchikov
This paper presents a new natural language processing task - Actionable Entities Recognition (AER) - recognition of entities that protagonists could interact with for further plot…
Connecting degree and polarity: An artificial language learning study
Lisa Bylinina, Alexey Tikhonov, Ekaterina Garmash
We investigate a new linguistic generalization in pre-trained language models (taking BERT (Devlin et al., 2019) as a case study). We focus on degree modifiers (expressions like sl…
Transformers in the loop: Polarity in neural models of language
Lisa Bylinina, Alexey Tikhonov
Representation of linguistic phenomena in computational language models is typically assessed against the predictions of existing linguistic theories of these phenomena. Using the…
EENLP: Cross-lingual Eastern European NLP Index
Alexey Tikhonov, Alex Malkhasov, Andrey Manoshin +4
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) pu…
DYPLODOC: Dynamic Plots for Document Classification
Anastasia Malysheva, Alexey Tikhonov, Ivan P. Yamshchikov
Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extracti…
Shape of Elephant: Study of Macro Properties of Word Embeddings Spaces
Alexey Tikhonov
Pre-trained word representations became a key component in many NLP tasks. However, the global geometry of the word embeddings remains poorly understood. In this paper, we demonstr…
It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning
Alexey Tikhonov, Max Ryabinin
Commonsense reasoning is one of the key problems in natural language processing, but the relative scarcity of labeled data holds back the progress for languages other than English.…
Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
Yana Agafonova, Alexey Tikhonov, Ivan P. Yamshchikov
This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine wit…
Artificial Neural Networks Jamming on the Beat
Alexey Tikhonov, Ivan P. Yamshchikov
This paper addresses the issue of long-scale correlations that is characteristic for symbolic music and is a challenge for modern generative algorithms. It suggests a very simple w…
Intuitive Contrasting Map for Antonym Embeddings
Igor Samenko, Alexey Tikhonov, Ivan P. Yamshchikov
This paper shows that, modern word embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors. This inf…
Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric
Ivan P. Yamshchikov, Viacheslav Shibaev, Nikolay Khlebnikov +1
The rapid development of such natural language processing tasks as style transfer, paraphrase, and machine translation often calls for the use of semantic similarity metrics. In re…
When Less Is More: Systematic Analysis of Cascade-Based Community Detection
Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak
Information diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe…
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures
Martijn Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova
Many cluster similarity indices are used to evaluate clustering algorithms, and choosing the best one for a particular task remains an open problem. We demonstrate that this proble…
Decomposing Textual Information For Style Transfer
Ivan P. Yamshchikov, Viacheslav Shibaev, Aleksander Nagaev +2
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 se…
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
Alexey Tikhonov, Viacheslav Shibaev, Aleksander Nagaev +2
This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation…
On Double-Sided QR-Codes
Alexey Tikhonov
Due to the widespread adoption of the smart mobile devices, QR codes have become one of the most-known types of 2D codes around the world. However, the data capacity properties of…
What is wrong with style transfer for texts?
Alexey Tikhonov, Ivan P. Yamshchikov
A number of recent machine learning papers work with an automated style transfer for texts and, counter to intuition, demonstrate that there is no consensus formulation of this NLP…