Search Results for author: Alexey Tikhonov

Found 24 papers, 12 papers with code

Post Turing: Mapping the landscape of LLM Evaluation

no code implementations3 Nov 2023 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.


BERT in Plutarch's Shadows

no code implementations10 Nov 2022 Ivan P. Yamshchikov, Alexey Tikhonov, Yorgos Pantis, Charlotte Schubert, Jürgen Jost

In particular, the Placita Philosophorum, together with one of the other Pseudo-Plutarch texts, shows similarities with the texts written by authors from an Alexandrian context (2nd/3rd century CE).

Language Modelling Philosophy

What is Wrong with Language Models that Can Not Tell a Story?

no code implementations9 Nov 2022 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 modern Natural Language Processing (NLP) and may even be in the whole field of Artificial Intelligence.

Good Classification Measures and How to Find Them

1 code implementation NeurIPS 2021 Martijn Gösgens, Anton Zhiyanov, Alexey Tikhonov, Liudmila Prokhorenkova

Several performance measures can be used for evaluating classification results: accuracy, F-measure, and many others.


Fine-Tuning Transformers: Vocabulary Transfer

1 code implementation29 Dec 2021 Vladislav Mosin, Igor Samenko, Alexey Tikhonov, Borislav Kozlovskii, Ivan P. Yamshchikov

Transformers are responsible for the vast majority of recent advances in natural language processing.

Transfer Learning

Actionable Entities Recognition Benchmark for Interactive Fiction

1 code implementation28 Sep 2021 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 development.

named-entity-recognition Named Entity Recognition +1

Connecting degree and polarity: An artificial language learning study

1 code implementation13 Sep 2021 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).

Language Modelling

Transformers in the loop: Polarity in neural models of language

1 code implementation ACL 2022 Lisa Bylinina, Alexey Tikhonov

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.

HeadlineCause: A Dataset of News Headlines for Detecting Causalities

1 code implementation LREC 2022 Ilya Gusev, Alexey Tikhonov

In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines.

Commonsense Causal Reasoning Common Sense Reasoning

EENLP: Cross-lingual Eastern European NLP Index

1 code implementation LREC 2022 Alexey Tikhonov, Alex Malkhasov, Andrey Manoshin, George Dima, Réka Cserháti, Md. Sadek Hossain Asif, Matt Sárdi

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.

Cross-Lingual Transfer Natural Language Inference +1

DYPLODOC: Dynamic Plots for Document Classification

no code implementations26 Jul 2021 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.

Classification Document Classification

It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning

1 code implementation22 Jun 2021 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.

Cross-Lingual Transfer

Shape of Elephant: Study of Macro Properties of Word Embeddings Spaces

no code implementations13 Jun 2021 Alexey Tikhonov

Pre-trained word representations became a key component in many NLP tasks.

Word Embeddings

Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity

1 code implementation13 Jul 2020 Yana Agafonova, Alexey Tikhonov, Ivan P. Yamshchikov

We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.

Text Generation

Artificial Neural Networks Jamming on the Beat

no code implementations13 Jul 2020 Alexey Tikhonov, Ivan P. Yamshchikov

It suggests a very simple workaround for this challenge, namely, generation of a drum pattern that could be further used as a foundation for melody generation.

Intuitive Contrasting Map for Antonym Embeddings

1 code implementation27 Apr 2020 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.

Word Embeddings

Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric

no code implementations10 Apr 2020 Ivan P. Yamshchikov, Viacheslav Shibaev, Nikolay Khlebnikov, Alexey Tikhonov

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.

Machine Translation Semantic Similarity +3

Decomposing Textual Information For Style Transfer

no code implementations WS 2019 Ivan P. Yamshchikov, Viacheslav Shibaev, Aleksander Nagaev, Jürgen Jost, Alexey Tikhonov

This paper focuses on latent representations that could effectively decompose different aspects of textual information.

Style Transfer

Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites

1 code implementation IJCNLP 2019 Alexey Tikhonov, Viacheslav Shibaev, Aleksander Nagaev, Aigul Nugmanova, Ivan P. Yamshchikov

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.

Style Transfer Text Style Transfer

What is wrong with style transfer for texts?

no code implementations13 Aug 2018 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 task.

BIG-bench Machine Learning Style Transfer

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