Instruction-tuning has become an integral part of training pipelines for...
Transferring information retrieval (IR) models from a high-resource lang...
Bilingual word lexicons are crucial tools for multilingual natural langu...
This paper investigates how Transformer language models (LMs) fine-tuned...
Linguistic acceptability (LA) attracts the attention of the research
com...
Recent advances in zero-shot and few-shot learning have shown promise fo...
The development of state-of-the-art systems in different applied areas o...
The recent advances in transfer learning techniques and pre-training of ...
We present the shared task on artificial text detection in Russian, whic...
The RuNNE Shared Task approaches the problem of nested named entity
reco...
The role of the attention mechanism in encoding linguistic knowledge has...
In the last year, new neural architectures and multilingual pre-trained
...
The vast majority of existing datasets for Named Entity Recognition (NER...
Practical needs of developing task-oriented dialogue assistants require ...
Recent research has adopted a new experimental field centered around the...
The impressive capabilities of recent generative models to create texts ...
In this paper, we present NEREL, a Russian dataset for named entity
reco...
Sub-tasks of intent classification, such as robustness to distribution s...
Robustness of huge Transformer-based models for natural language process...
The new generation of pre-trained NLP models push the SOTA to the new li...
The outstanding performance of transformer-based language models on a gr...
This paper presents a new Massive Open Online Course on Natural Language...
The success of pre-trained transformer language models has brought a gre...
Annotating training data for sequence tagging tasks is usually very
time...
Real-life applications, heavily relying on machine learning, such as dia...
We show-case an application of information extraction methods, such as n...
In this paper, we introduce an advanced Russian general language
underst...
We study the effectiveness of contextualized embeddings for the task of
...
DaNetQA, a new question-answering corpus, follows (Clark et. al, 2019)
d...
In this paper we present a corpus of Russian strategic planning document...
In this paper we present a corpus of Russian strategic planning document...
Neural Architecture Search (NAS) is a promising and rapidly evolving res...
In this paper, our focus is the connection and influence of language
tec...
Applications such as machine translation, speech recognition, and inform...
Disambiguation of word senses in context is easy for humans, but is a ma...
We explore the abilities of character recurrent neural network (char-RNN...
We investigate the performance of sentence embeddings models on several ...
In this paper we tackle multilingual named entity recognition task. We u...