Papers with French-English

5 papers
Addressing the Vulnerability of NMT in Input Perturbations (2021.naacl-industry)

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Challenge: Recent advances in NMT have improved translation quality but are vulnerable to input perturbations.
Approach: They propose a method to reduce the effect of noisy inputs by using a Context-Enhanced Reconstruction approach.
Outcome: The proposed approach improves robustness on Chinese-English and French-English translation tasks.
Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction (C18-1)

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Challenge: Existing models for parallel data harvesting from Wikipedia are language-independent, robust and highly scalable.
Approach: They propose an end-to-end neural model for large-scale parallel data harvesting from Wikipedia . their model is language-independent, robust, and highly scalable .
Outcome: The proposed model is language-independent, robust, and highly scalable.
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)

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Challenge: tense inconsistency is a common problem in machine translation systems.
Approach: They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy.
Outcome: The proposed model can measure the tense consistency performance of machine translation systems for the first time.
Document Sub-structure in Neural Machine Translation (2020.lrec-1)

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Challenge: Current approaches to machine translation (MT) translate sentences in isolation, disregarding context they appear in, or model context at the level of the full document.
Approach: They propose to include information about the topic of the section within which each sentence is found in a document that is not homogeneous . they use a cache-based model to model the context of the document, instead of translating sentences in isolation .
Outcome: The proposed model incorporates information about the topic of the section within which each sentence is found into a neural model.
Gaperon: A Peppered English-French Generative Language Model Suite (2026.findings-acl)

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Challenge: Standardized benchmarks have become the dominant metric for measuring progress in large language models, but their validity is compromised by data contamination and unclear relationship between benchmark scores and genuine language understanding.
Approach: They propose to use GAPERON to investigate evaluation dynamics under realistic training conditions.
Outcome: The proposed model outperforms models that excel on benchmarks in qualitative text generation and vice versa.

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