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Found 35 papers, 15 papers with code
ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection
We describe the ULFRI system used in the Subtask 1 of SemEval-2022 Task 4 Patronizing and condescending language detection.
BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers
Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions.
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
Unsupervised Approach to Multilingual User Comments Summarization
The research on the summarization of user comments is still in its infancy, and human-created summarization datasets are scarce, especially for less-resourced languages.
Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces
Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation.
Retrieval-augmented code completion for local projects using large language models
We evaluate In-context retrieval-augmented generation on larger models and conclude that, despite its simplicity, the approach is more suitable than using the RETRO architecture.
Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies
As cross-lingual transfer strategies, we compare the intermediate-training (\textit{IT}) that uses each language sequentially and cross-lingual validation (\textit{CLV}) that uses a target language already in the validation phase of fine-tuning.
One model to rule them all: ranking Slovene summarizers
We propose a system that recommends the most suitable summarization model for a given text.
Detection of depression on social networks using transformers and ensembles
As the impact of technology on our lives is increasing, we witness increased use of social media that became an essential tool not only for communication but also for sharing information with community about our thoughts and feelings.
Feature construction using explanations of individual predictions
Feature construction can contribute to comprehensibility and performance of machine learning models.
Unified Question Answering in Slovene
Most approaches are developed for English, while less-resourced languages are much less researched.
Review of Natural Language Processing in Pharmacology
As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology.
Sequence to sequence pretraining for a less-resourced Slovenian language
Large pretrained language models have recently conquered the area of natural language processing.
Slovene SuperGLUE Benchmark: Translation and Evaluation
We present a Slovene combined machine-human translated SuperGLUE benchmark.
Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages
To analyze the importance of focusing on a single language and the importance of a large training set, we compare created models with existing monolingual and multilingual BERT models for Estonian, Latvian, and Lithuanian.
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing
We propose a novel methodology for the extraction of paraphrasing datasets from NLI datasets and cleaning existing paraphrasing datasets.
Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness.
Evaluation of contextual embeddings on less-resourced languages
The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives.
Cross-lingual alignments of ELMo contextual embeddings
Building machine learning prediction models for a specific NLP task requires sufficient training data, which can be difficult to obtain for less-resourced languages.
Cross-lingual Transfer of Abstractive Summarizer to Less-resource Language
Automatic evaluation shows that the summaries of our best cross-lingual model are useful and of quality similar to the model trained only in the target language.
Enhancing deep neural networks with morphological information
We analyse the effect of adding morphological features to LSTM and BERT models.
MICE: Mining Idioms with Contextual Embeddings
Idiomatic expressions can be problematic for natural language processing applications as their meaning cannot be inferred from their constituting words.
FinEst BERT and CroSloEngual BERT: less is more in multilingual models
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems.
Propositionalization and Embeddings: Two Sides of the Same Coin
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches.
Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders
Due to complex experimental settings, missing values are common in biomedical data.
AttViz: Online exploration of self-attention for transparent neural language modeling
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists.
Multilingual Culture-Independent Word Analogy Datasets
In text processing, deep neural networks mostly use word embeddings as an input.
High Quality ELMo Embeddings for Seven Less-Resourced Languages
Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task.
Prediction Uncertainty Estimation for Hate Speech Classification
As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased.
Generating Data using Monte Carlo Dropout
In many such cases, generators of synthetic data with the same statistical and predictive properties as the actual data allow efficient simulations and development of tools and applications.
Supervised and Unsupervised Neural Approaches to Text Readability
We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents.
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
Network node embedding is an active research subfield of complex network analysis.
Identifying roles of clinical pharmacy with survey evaluation
Next, we build a model for predicting a successful introduction of clinical pharmacy to the clinical departments.
Data Generators for Learning Systems Based on RBF Networks
The proposed generator is based on RBF networks, which learn sets of Gaussian kernels.
