Papers by Ioan Calapodescu
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)
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| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
| Approach: | They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on the Rotowire NLG task. |
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness (D19-56)
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Alexandre Berard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier, Vassilina Nikoulina
| Challenge: | BLEU: MT is a very robust and efficient way to translate user-generated content. |
| Approach: | They propose a task to encourage research on MT robustness and domain adaptation . they ask professionals to translate 11.5k french 4SQ reviews to English . |
| Outcome: | The proposed task improves on the existing MT systems in a real-world scenario . the proposed methods improve translation accuracy and sentiment analysis . |
Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection (2025.coling-main)
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| Challenge: | Existing methods for crowdsourcing data collection require a human workforce, which is hard to sustain. |
| Approach: | They propose to use Speech Foundation Models to automate validation processes . they find that SFMs can reduce reliance on human validation . |
| Outcome: | The proposed model reduces the reliance on human validation without degrading the quality of the final data. |
Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
| Approach: | They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown . |
| Outcome: | The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs. |
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Current research in NMT Domain Adaptation rarely provides insights on the amount of data required to perform Domain . |
| Approach: | They propose a Domain adaptation learning curve prediction model that predicts prospective DA performance based on in-domain monolingual samples in the source language. |
| Outcome: | The proposed model predicts prospective DA performance based on in-domain monolingual samples in the source language. |