Papers by Antonio Valerio Miceli Barone
Improving Machine Translation of Educational Content via Crowdsourcing (L18-1)
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Maximiliana Behnke, Antonio Valerio Miceli Barone, Rico Sennrich, Vilelmini Sosoni, Thanasis Naskos, Eirini Takoulidou, Maria Stasimioti, Menno van Zaanen, Sheila Castilho, Federico Gaspari, Panayota Georgakopoulou, Valia Kordoni, Markus Egg, Katia Lida Kermanidis
| Challenge: | Using crowdsourcing to train neural machine translation models is expensive and expensive . professional outsourcing of bilingual data is expensive if the translations are of a lower quality . |
| Approach: | They analyze the impact of crowdsourcing on the quality of in-domain training data . they use translations of MOOCs from English to eleven languages to fine-tune machine translation models . |
| Outcome: | The proposed method improves on general-domain training data and with pre-existing in-domain corpora. |
The Larger they are, the Harder they Fail: Language Models do not Recognize Identifier Swaps in Python (2023.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used for programming tasks but lack a deep understanding of the content they manipulate. |
| Approach: | They show that LLMs fail to correctly generate correct Python code when default function names are swapped . they also show that they become more confident in their incorrect predictions as the model size increases . |
| Outcome: | The proposed models fail to generate correct Python code when default function names are swapped, and become more confident in their incorrect predictions as the model size increases. |
DISCOSQA: A Knowledge Base Question Answering System for Space Debris based on Program Induction (2023.acl-industry)
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| Challenge: | a system that can answer complex natural language queries is developed for the European Space Agency . space debris are uncontrolled artificial objects left in orbit during normal operations or due to malfunctions . |
| Approach: | They propose a query-based system that can answer queries in natural language . it generates a program sketch from a natural language question and executes it against the database . |
| Outcome: | The proposed system can answer queries in natural language based on a natural language question generated by a query program . the system reduces overfitting and shortcut learning even with limited training data, the authors say . |