Papers by Mark Last
Detecting Troll Tweets in a Bilingual Corpus (2020.lrec-1)
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| Challenge: | a large amount of troll accounts have emerged with efforts to manipulate public opinion on social network sites . a recent study found that trolled tweets spread misinformation, fake news, and propaganda . we use supervised classification to detect trol tweets in both English and Russian . |
| Approach: | They propose to detect troll tweets in English and Russian using machine learning algorithms . they use monolingual, cross-lingual, and bilingual training scenarios . |
| Outcome: | The proposed method uses monolingual, cross-lingual, and bilingual training scenarios. |
Using Graphs for Word Embedding with Enhanced Semantic Relations (D19-53)
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| Challenge: | Word embedding algorithms are based on sequential text input, while others are utilizing a graph representation of text. |
| Approach: | They propose a word embedding algorithm based on a directed word graph to provide additional information for sequential text input algorithms. |
| Outcome: | The proposed algorithm is based on a directed word graph to provide additional information for sequential text input algorithms. |
Transitive self-consistency evaluation of NLI models without gold labels (2025.emnlp-main)
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| Challenge: | Recent studies show that NLI models make inconsistent model decisions during inference . previous studies tested only variations of two transitive consistency rules out of four . |
| Approach: | They propose to generate adversarial examples using antonym replacements to test transitive consistency of NLI models. |
| Outcome: | The proposed approach can test all four rules automatically by generating adversarial examples via antonym replacements. |
Embible: Reconstruction of Ancient Hebrew and Aramaic Texts Using Transformers (2024.findings-eacl)
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| Challenge: | inscriptions are damaged over time due to earthquakes, fires, political conflicts, and other natural and human-related causes. |
| Approach: | They evaluate masked language modeling approach to reconstruct illegible inscriptions . they use transformer-based models to fine-tune models on Biblical texts . |
| Outcome: | The proposed model achieves the highest text completion accuracy on masked parts of the Biblical texts. |