Search Results for author:
Found 10 papers, 7 papers with code
Czert – Czech BERT-like Model for Language Representation
This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures.
Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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End-to-end Multilingual Coreference Resolution with Mention Head Prediction
This paper describes our approach to the CRAC 2022 Shared Task on Multilingual Coreference Resolution.
Findings of the Shared Task on Multilingual Coreference Resolution
The public edition of CorefUD 1. 0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data.
MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain
This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites.
Czech News Dataset for Semantic Textual Similarity
We describe the process of collecting and annotating the data in detail.
Multilingual Coreference Resolution with Harmonized Annotations
In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models -- for Slavic languages and for all the languages together.
Czert -- Czech BERT-like Model for Language Representation
This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures.
UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation
In this paper, we describe our method for detection of lexical semantic change (i. e., word sense changes over time) for the DIACR-Ita shared task, where we ranked $1^{st}$.
UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.}
