Papers by Lidia Pivovarova
Benchmarks and models for entity-oriented polarity detection (N18-3)
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| Challenge: | a dataset of 17,000 manually labeled documents is large for determining entity-oriented polarity in business news. |
| Approach: | They propose a convolutional neural network-based approach to classify entity-oriented polarity in business news. |
| Outcome: | The proposed model is based on convolutional neural networks and is small on the scale of existing models. |
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings (2022.coling-1)
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| Challenge: | a novel neural topic model for comparable data maps texts from multiple languages and images into a shared topic space. |
| Approach: | They propose a novel multimodal multilingual neural topic model that maps texts from multiple languages and images into a shared topic space. |
| Outcome: | The proposed model outperforms a zero-shot topic model in predicting topic distributions for comparable multilingual data and performs as well on unaligned embeddings as it does on aligned embeds. |
Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark (2026.eacl-long)
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| Challenge: | This paper presents a novel task of extracting low-resourced and noisy Latin fragments from mixed-language historical documents with varied layouts. |
| Approach: | They propose to extract low-resourced and noisy Latin fragments from mixed-language historical documents with varied layouts using a multimodal dataset. |
| Outcome: | The proposed model lacks a functional comprehension of Latin, but reliable detection is achievable with zero-shot models. |
Scalable and Interpretable Semantic Change Detection (2021.naacl-main)
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| Challenge: | Several cluster-based methods for word usage change detection are unscalable in terms of memory consumption and computation time. |
| Approach: | They propose a scalable method for word usage-change detection that uses contextual embeddings to aggregate word usages into clusters. |
| Outcome: | The proposed method offers high performance and interpretability while being unscalable. |
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings (2022.lrec-1)
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| Challenge: | Neural approaches for natural language generation (NLG) have mushroomed due to large textual resources. |
| Approach: | They propose to use a pretrained multilingual encoder-decoder model and a combination of two pretrained language models to train a model in a low-resource setting. |
| Outcome: | The proposed model outperforms the previous model on English and on a small subset of the same data. |