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Found 22 papers, 11 papers with code
Towards the Data-driven System for Rhetorical Parsing of Russian Texts
Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank {--} first Russian corpus annotated within RST framework {--} are presented.
A Dataset for Noun Compositionality Detection for a Slavic Language
This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds.
Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates
We build the first full pipeline for semantic role labelling of Russian texts.
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages.
Neural Entity Linking: A Survey of Models Based on Deep Learning
This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language processing.
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Annotating training data for sequence tagging of texts is usually very time-consuming.
How Certain is Your Transformer?
In this work, we consider the problem of uncertainty estimation for Transformer-based models.
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.
Towards Computationally Feasible Deep Active Learning
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition.
Medical Image Captioning via Generative Pretrained Transformers
The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
Active Learning for Abstractive Text Summarization
Active Learning (AL) is a technique developed to reduce the amount of annotation required to achieve a certain level of machine learning model performance.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
These results show that the problem is far from solved and that there is a lot of room for improvement.
LM-Polygraph: Uncertainty Estimation for Language Models
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields.
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis
While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
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.
Generating Lexical Representations of Frames using Lexical Substitution
We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs.
