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Uncertainty Estimation in Autoregressive Structured Prediction
P. 1–31.
Andrey Malinin, Gales M.
Golyadkin M., Innokentiy Humonen, Rubanova V. et al., , in: MM '25: Proceedings of the 33rd ACM International Conference on Multimedia.: Association for Computing Machinery (ACM), 2025. P. 12875–12881.
We present the first multimodal dataset MuMMy, for developing research assistants that can interpret Egyptian hieroglyphic texts. It pairs images with Gardiner codes, transliteration, and English translation at two levels of granularity. We also evaluate several deep learning pipelines across OCR, transliteration, and translation tasks, revealing the complexity of the domain and the challenges posed ...
Added: November 8, 2025
Fadeeva E., Vashurin R., Tsvigun A. et al., , in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.: Singapore: Association for Computational Linguistics, 2023. P. 446 –461.
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often “hallucinate”, i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. Uncertainty estimation (UE) methods are one ...
Added: February 17, 2025
Tatiana Sherstinova, Nikolay Mikhaylovskiy, Evgenia Kolpashchikova et al., , in: Proceedings of the 35th Conference of Open Innovations Association FRUCT, 24-26 April 2024, Tampere, FinlandIssue 1.: FRUCT Oy, 2024. P. 253–258.
Contemporary advancements in NLP and neural network techniques are paving the way to enhance and harness traditional linguistic resources and corpora, as well as expand the methods of applying neural networks for complex language material. Thus, a weak point for both theoretical and applied linguistic tasks is the processing of spontaneous everyday speech. Two experiments ...
Added: November 29, 2024
Shuranov E., / Series Computer Science "arxiv.org". 2021.
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: February 14, 2023
Association for Computational Linguistics, 2022.
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural ...
Added: May 17, 2022
Copenhagen, Denmark: CEUR Workshop Proceedings, 2021.
The second workshop on Crowd Science is organized in conjunction with the 47th International Conference on Very Large Data Bases (VLDB 2021). This workshop is the second in a series of events that has the goal of helping crowdsourcing “transition” from art to science, and tackles the research challenges that we face to make crowdsourcing ...
Added: December 13, 2021
Плетенев С. А., В кн.: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 16–19 июня 2021 г.)Issue 20.: Russian State University for the Humanitie, 2021.
Added: December 13, 2021
Malinin A., Gales M., , in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019).: [б.и.], 2019.
Added: November 1, 2021
Malinin A., Mlodozeniec B., Gales M., , in: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020).: ICLR, 2020.
Added: November 1, 2021
Ryabinin M., Malinin A., Gales M., , in: Advances in Neural Information Processing Systems 34 (NeurIPS 2021).: Curran Associates, Inc., 2021. P. 6023–6035.
Added: October 31, 2021
Sokolov A., Savchenko A., , in: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2021. P. 413–418.
This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male ...
Added: September 26, 2021
Meyer J., Rauchenstein L., Eisenberg J., , in: Proceedings of The 12th Language Resources and Evaluation ConferenceVol. 12.: European Language Resources Association (ELRA), 2020. P. 6462–6468.
We describe the creation of the Artie Bias Corpus, an English dataset of expert-validated <audio, transcript> pairs with demographic tags for age, gender, accent. We also release open software which may be used with the Artie Bias Corpus to detect demographic bias in Automatic Speech Recognition systems, and can be extended to other speech technologies. ...
Added: April 20, 2021
Association for Computational Linguistics, 2019.
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). ...
Added: January 7, 2021
Sokolov A., / Series Computer Science "arxiv.org". 2021.
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: November 17, 2020
CEUR Workshop Proceedings, 2020.
The International Conference “Internet and Modern Society” (IMS-2020) was initially planned to take place in St. Petersburg, Russia. Due to the spread of COVID-19 and the ban on public events, the conference was held during 17-20 June 2020 in the format of online sessions with a discussion of papers and presentations uploaded in advance. The ...
Added: November 1, 2020
Sokolov A., Savchenko A., , in: 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2019. Ch. 19 P. 113–116.
In this article, we focus on the isolated voice command recognition for autonomous man-machine and intelligent robotic systems. We propose to create a grammar model for a small testing command set with self-loops for each state to return blank symbols for noise and out-of-vocabulary words. In addition, we use single arc connected beginning and ending ...
Added: October 21, 2019
Savchenko L.V., Savchenko A.V., Journal of Communications Technology and Electronics 2019 Vol. 64 No. 3 P. 238–244
In this paper, we studied the phonetic approach for voice processing. A method for automatic recognition
of speech signals, in which each quasistationary segment is associated with a fuzzy set of phonemes,
was developed. We proposed the operation of the probabilistic triangular norm for fuzzy sets corresponding
to the input frame and the nearest reference phoneme. The developed ...
Added: June 7, 2019
Mescheryakova E.I., Nesterenko L.V., , in: Computational Linguistics and Intellectual Technologies. International Conference "Dialogue 2017" ProceedingsVol. 1. Issue 16 (23).: M.: -, 2017.
Added: January 4, 2019
Galliani P., Dezfouli A., Bonilla E. et al., , in: Proceedings of Machine Learning Research. 2017. Volume 54: Artificial Intelligence and StatisticsVol. 54: Artificial Intelligence and Statistics.: [б.и.], 2017. P. 353–361.
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods. Our approach does not need to know the details of the structured likelihood model and can scale up to a large number of observations. Furthermore, we show that the required expected likelihood term and its ...
Added: December 10, 2018