Papers by Proyag Pal
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation (2022.naacl-main)
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| Challenge: | a neural machine translation system generates a translation t in the target language, but for any sentence of non-trivial complexity, the translation s is not unique. |
| Approach: | They propose a method to quantify the amount of information missing in a machine translation system. |
| Outcome: | The proposed model captures extra information from a single float representation of the target sentence and reproduces it with two 32-bit floats per target token. |
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025.acl-long)
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Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya Malik, Farrokh Mehryary, Vladislav Mikhailov, Nikita Moghe, Amanda Myntti, Dayyán O’Brien, Stephan Oepen, Proyag Pal, Jousia Piha, Sampo Pyysalo, Gema Ramírez-Sánchez, David Samuel, Pavel Stepachev, Jörg Tiedemann, Dušan Variš, Tereza Vojtěchová, Jaume Zaragoza-Bernabeu
| Challenge: | a large number of textual data is needed to train state-of-the-art large language models. |
| Approach: | They propose a collection of monolingual and parallel corpora from the Internet Archive . they document the entire data pipeline and release the code to reproduce it . |
| Outcome: | The proposed collection of monolingual and parallel corpora is based on the HPLT v2 dataset . it includes 8T tokens covering 193 languages and 380M sentence pairs covering 51 languages . |
Document-Level Machine Translation with Large-Scale Public Parallel Corpora (2024.acl-long)
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| Challenge: | Document-level machine translation has inherent advantages over sentence-level translation due to additional information available to a model from document context. |
| Approach: | They propose to use document context to train context-aware models on these datasets and to use it to model document-level phenomena. |
| Outcome: | The proposed models improve translation quality and target document-level phenomena by incorporating contextual information from several preceding sentences. |
Cheating to Identify Hard Problems for Neural Machine Translation (2023.findings-eacl)
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| Challenge: | We find that the most frequent tokens are not necessarily the most accurately translated due to these often being function words and punctuation that can be used more flexibly in translation. |
| Approach: | They propose a method to provide a compressed representation of the target as an input and a second method to fine-tune a standard transformer model. |
| Outcome: | The proposed method produces models with higher scores, but with more errors . the proposed method is based on two forms of cheating: providing a compressed representation of the target as an input and fine-tuning on the test set. |