Papers by Antonio Laverghetta Jr.
A Large-Scale Study of Machine Translation in Turkic Languages (2021.emnlp-main)
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Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan
| Challenge: | a large corpus covering 22 Turkic languages is included in this paper . low-resource MT evaluation has traditionally focused on European languages due to limitations of available technology and resources. |
| Approach: | They present a case study of the practical application of MT in the Turkic language family . they propose to realize the gains of NMT for Turkic languages under high-resource to extremely low-resourced scenarios. |
| Outcome: | The proposed study shows that the new methods can be used in the Turkic language family . the results highlight bottlenecks in building competitive systems . |
Can Transformer Language Models Predict Psychometric Properties? (2021.starsem-1)
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| Challenge: | Transformer-based language models (LMs) are gaining popularity on many NLP benchmark tasks. |
| Approach: | They use human responses to calculate psychometric properties of test items . they find transformer-based LMs predict psychometric property consistently well . |
| Outcome: | The transformer-based language models are able to predict psychometric properties of test items . the models can predict psychometries well in certain categories but poorly in others . |
Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks (2020.aacl-srw)
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| Challenge: | Recent advances on natural language processing (NLP) benchmarks have been driven by increasingly sophisticated language models, which are pretrained on enormous amounts of data before use. |
| Approach: | They propose to develop a task-agnostic model for problem difficulty and apply it to the Stanford Natural Language Inference dataset. |
| Outcome: | The proposed model predicts how many annotators will answer a question correctly and then projectes the difficulty estimates onto the full SNLI train set to create the curriculum. |
Developmental Negation Processing in Transformer Language Models (2022.acl-short)
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| Challenge: | Negation is an important construct in language for reasoning over the truth of propositions, garnering interest from philosophy (Horn, 1989) and psycholinguistics (Zwaan, 2012). |
| Approach: | They propose to frame a natural language inference task as a problem and examine how well transformers can process negation categories. |
| Outcome: | The proposed models perform better on certain categories, suggesting clear differences in how they are processed. |
Creative Preference Optimization (2025.findings-emnlp)
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Mete Ismayilzada, Antonio Laverghetta Jr., Simone A. Luchini, Reet Patel, Antoine Bosselut, Lonneke Van Der Plas, Roger E. Beaty
| Challenge: | Existing methods for enhancing LLM creativity focus on diversity or specific tasks, failing to address creativity’s multifaceted nature in a generalizable way. |
| Approach: | They propose a method that injects signals from multiple creativity dimensions into the preference optimization objective in a modular fashion. |
| Outcome: | The proposed method outperforms baseline models on automated and human evaluations while maintaining high output quality. |