Papers by Vassilina Nikoulina
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
| Outcome: | The proposed method keeps its virtuous characteristics but is slower than softmax and achieves on par or better performance in machine translation task. |
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? (2021.findings-acl)
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| Challenge: | Recent studies on multilingual representations focus on whether there is an emergence of language-independent representations or whether multilingual models partition their weights among different languages. |
| Approach: | They analyze encoder self-attention and encoder-decoder attention heads in a multilingual neural translation model. |
| Outcome: | The proposed model is based on a multilingual neural translation model with a language-independent representation. |
What Do Compressed Multilingual Machine Translation Models Forget? (2022.findings-emnlp)
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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Recent studies show that pre-trained models achieve state-of-the-art results in NLP tasks but their size makes it more challenging to apply them in resource-constrained environments. |
| Approach: | They assess the impact of compression methods on multilingual Neural Machine Translation models for various language groups, gender, and semantic biases. |
| Outcome: | The proposed compression methods improve models on different benchmarks for language groups, gender, and semantic biases. |
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages (2022.emnlp-main)
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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Existing models for multilingual machine translation use scaling up the number of parameters to overcome the curse of multilinguality. |
| Approach: | They propose a multilingual machine translation model that shares information between similar languages and scales up the number of parameters to overcome the curse of multilinguality. |
| Outcome: | The proposed model outperforms previous models on low-resource benchmarks while improving inference latency and memory usage. |
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 . |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation (2024.findings-emnlp)
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David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Stéphane Clinchant, Vassilina Nikoulina
| Challenge: | Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. |
| Approach: | They propose a library that allows to benchmark and standardize RAG experiments. |
| Outcome: | The proposed library is an end-to-end library for reproducible research standardizing RAG experiments. |
Efficient Inference for Multilingual Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Multilingual NMT is an attractive solution for production, but to match bilingual quality, it comes at the cost of larger and slower models. |
| Approach: | They propose to use a shallow decoder with vocabulary filtering to speed up inference . they validate their findings with BLEU and chrF on 380 language pairs . |
| Outcome: | The proposed approach can be used in two 20-language multi-parallel settings. |
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. |
On the use of BERT for Neural Machine Translation (D19-56)
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| Challenge: | Existing studies on using pretrained language models for supervised NMT have not been successful. |
| Approach: | They propose to integrate BERT pretrained models with supervised NMT models by using monolingual data. |
| Outcome: | The proposed models improve translation quality in English-German, English-Russian and IWSLT14 datasets. |
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model (2023.acl-long)
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| Challenge: | Neural Machine Translation models are based on a Mixture of Experts architecture and can be pruned to remove up to 80% of experts without further finetuning. |
| Approach: | They propose a pruning method that removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
| Outcome: | The proposed pruning method removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting (2023.acl-long)
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Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, Alham Fikri Aji, David Ifeoluwa Adelani, Khalid Almubarak, M Saiful Bari, Lintang Sutawika, Jungo Kasai, Ahmed Baruwa, Genta Winata, Stella Biderman, Edward Raff, Dragomir Radev, Vassilina Nikoulina
| Challenge: | Existing language adaptation strategies for multilingual models are limited to 46 languages . a new language is added to the model to improve zero-shot prompting performance . |
| Approach: | They apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. |
| Outcome: | The proposed model can be extended to other languages without incurring prohibitively large costs. |