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20192026
most citedEnhancing Sequence-to-Sequence Neural Lemmatization with External Resources

6 citations · 9 across the 6 of their papers we have counts for

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cs.CL2026

Pantagruel: Unified Self-Supervised Encoders for French Text and Speech

Phuong-Hang Le, Valentin Pelloin, Arnault Chatelain +27

We release Pantagruel models, a new family of self-supervised encoder models for French text and speech. Instead of predicting modality-tailored targets such as textual tokens or s…

cs.CL2024★ 1 cited

Evaluating Lexicon Incorporation for Depression Symptom Estimation

Kirill Milintsevich, Gaël Dias, Kairit Sirts

This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon informati…

cs.CL2024★ 2 cited

Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset

Kirill Milintsevich, Kairit Sirts, Gaël Dias

This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. While previous research relies on…

cs.CL2021★ 6 cited

Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources

Kirill Milintsevich, Kairit Sirts

We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. During…

cs.CL2020

Evaluating Multilingual BERT for Estonian

Claudia Kittask, Kirill Milintsevich, Kairit Sirts

Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Es…

cs.CL2019

Automated Word Stress Detection in Russian

Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak +1

In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with L…

cs.CL2019

Char-RNN for Word Stress Detection in East Slavic Languages

Ekaterina Chernyak, Maria Ponomareva, Kirill Milintsevich

We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russia…