Challenge: Existing models that only use auxiliary languages to encourage multilingual agreement ignore the relationships between different language pairs.
Approach: They propose a multilingual agreement-based method which explicitly models the agreement between different translation directions by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages.
Outcome: The proposed method improves on the multilingual translation task of 10 language pairs.

Similar Papers

Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations (2022.naacl-main)

Copied to clipboard

Challenge: Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions.
Approach: They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning.
Outcome: The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection.
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)

Copied to clipboard

Challenge: Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer.
Approach: They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue .
Outcome: The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages.
Learn and Consolidate: Continual Adaptation for Zero-Shot and Multilingual Neural Machine Translation (2023.emnlp-main)

Copied to clipboard

Challenge: Existing multilingual neural machine translation models perform poorly on language pairs with no parallel corpus.
Approach: They propose a two-stage approach that encourages original models to acquire language-agnostic multilingual representations from new data and preserves the model architecture without introducing parameters.
Outcome: The proposed approach improves performance in translation directions where existing models are weak and mitigates degeneration in the well-performing translation directions, offering flexibility in the real-world scenario.
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)

Copied to clipboard

Challenge: Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance.
Approach: They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss.
Outcome: The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl.
From Bilingual to Multilingual Neural Machine Translation by Incremental Training (P19-2)

Copied to clipboard

Challenge: Existing approaches to multilingual neural machine translation are based on task specific models and the addition of one more language is only possible by retraining the whole system.
Approach: They propose a training schedule that scales to more languages without modification of previous components.
Outcome: The proposed training schedule shows close results to state-of-the-art in the WMT task.
Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation (2025.acl-long)

Copied to clipboard

Challenge: Multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages.
Approach: They propose a method that inserts a set of tokens specifying the target language into the input sequence between the source and target tokens.
Outcome: The proposed method outperforms existing models on a large-scale benchmark.
Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)

Copied to clipboard

Challenge: Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning.
Approach: They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs.
Outcome: The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs.
Multilingual Neural Machine Translation (2020.coling-tutorials)

Copied to clipboard

Challenge: In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation.
Approach: They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting .
Outcome: This tutorial will cover the latest advances in NMT to enhance low-resource translation models.
Unifying the Convergences in Multilingual Neural Machine Translation (2022.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to multilingual neural machine translation are overfitting and inconsistency is ignored .
Approach: They propose a training strategy that picks up language-specific best checkpoints for each language pair to teach the current model on the fly.
Outcome: The proposed training strategy alleviates convergence inconsistency and achieves state-of-the-art on language pairs.
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)

Copied to clipboard

Challenge: Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model.
Approach: They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders.
Outcome: The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations