Papers by Aleksander Wawer
Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? (D19-66)
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| Challenge: | Using psycholinguistic features to distinguish lies from true statements is a difficult task and a problem to be solved. |
| Approach: | They compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements using data from a large ongoing study. |
| Outcome: | The proposed methods outperform both fact checking and human baselines but the accuracy is not high. |
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
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| Challenge: | Existing work on abstractive dialogue summarizations has focused on news summarizing but there is no such comprehensive dataset. |
| Approach: | They propose to use a chat-dialogues corpus with abstractive dialogue summaries to generate a short version of text that covers the main points succinctly. |
| Outcome: | The proposed dataset achieves higher ROUGE scores than the model-generated summaries of news, compared with human evaluators' judgement. |
The Linguistic Category Model in Polish (LCM-PL) (L18-1)
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| Challenge: | a new version of the Linguistic Category Model (LCM) dictionary for the Polish language is available for use and integrates with the Polish WordNet. |
| Approach: | They propose to use a dictionary that is annotated manually in its most important parts . they propose to add more manually annotating senses and increase quality of automated annotations . |
| Outcome: | The proposed dictionary is the first widely usable version of the resource . it will have more manually annotated senses and more automated annotations . |
Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)
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Aleksander Leczkowski, Justyna Grudzińska, Manuel Vargas Guzmán, Aleksander Wawer, Aleksandra Siemieniuk
| Challenge: | Existing work on how to integrate world knowledge into a QSD model has been limited . |
| Approach: | They use a scope-disambiguated corpus annotated with prepositional senses to integrate our knowledge into a machine learning model. |
| Outcome: | The proposed model is based on a scope-disambiguated corpus annotated with prepositional senses . Statistical analysis shows that prepositions have a positive impact on the learnability of automatic QSD systems. |