Papers by Alina Wróblewska

5 papers
Polish Corpus of Annotated Descriptions of Images (L18-1)

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Challenge: a new dataset of image descriptions is presented in Polish . the dataset is too small for training a sophisticated language-vision system.
Approach: They propose to use a Polish dataset to analyze image descriptions . the descriptions are morphosyntactically analysed and annotated by human annotators .
Outcome: The proposed model learns about the inter-modal correspondences between language and vision.
Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020.lrec-1)

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Challenge: a paper aims at enriching the manually annotated part of National Corpus of Polish with a syntactic layer.
Approach: They enrich manually annotated part of Polish National Corpus with a syntactic layer and a UD dependency graph.
Outcome: The proposed model outperforms a model trained on a smaller set of gold-standard trees in predicting part-of-speech tags, morphological features, lemmata and labelled dependency trees.
Empirical Linguistic Study of Sentence Embeddings (P19-1)

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Challenge: a new method of analysing sentence embeddings shows that linguistic information is retained in the vector representations of sentences.
Approach: They propose a method of analysing the content of sentence embeddings based on probing tasks and contrasting languages.
Outcome: The proposed method is based on probing tasks and classification datasets for two contrasting languages.
NLPre: A Revised Approach towards Language-centric Benchmarking of Natural Language Preprocessing Systems (2024.lrec-main)

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Challenge: GLUE benchmarking system enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance.
Approach: They propose a language-centric benchmarking system that enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance.
Outcome: The proposed system is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark.
COMBO: State-of-the-Art Morphosyntactic Analysis (2021.emnlp-demo)

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Challenge: COMBO is an end-to-end NLP system for accurate part-of-speech tagging, morphological analysis, and (enhanced) dependency parsing.
Approach: They propose a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing.
Outcome: The proposed system predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers.

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