MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality
Does multilingual Neural Machine Translation (NMT) lead to The Curse of the Multlinguality or provides the Cross-lingual Knowledge Transfer within a language family? In this study, we explore multiple approaches for extending the available data-regime in NMT and we prove cross-lingual benefits even in 0-shot translation regime for low-resource languages. With this paper, we provide state-of-the-art open-source NMT models for translating between selected Slavic languages. We released our models on the HuggingFace Hub (https://hf.co/collections/allegro/multislav-6793d6b6419e5963e759a683) under the CC BY 4.0 license. Slavic language family comprises morphologically rich Central and Eastern European languages. Although counting hundreds of millions of native speakers, Slavic Neural Machine Translation is under-studied in our opinion. Recently, most NMT research focuses either on: high-resource languages like English, Spanish, and German - in WMT23 General Translation Task 7 out of 8 task directions are from or to English; massively multilingual models covering multiple language groups; or evaluation techniques.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTTransfer LearningTranslationSimilar Papers 제목 키워드 기반
Multilingual Evidence Retrieval and Fact Verification to Combat Global Disinformation: The Power of Polyglotism
This article investigates multilingual evidence retrieval and fact verification as a step to combat global disinformation, a first effort of this kind, to the best of our knowledge. The goal is building multilingual syst…
Cross-Lingual TransferFact VerificationRetrievalTransfer LearningClaim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering
Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. While tasks such as claim matching and fact-checked claim retrieval a…
Contrastive LearningAnalyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models
Recent advances in training multilingual language models on large datasets seem to have shown promising results in knowledge transfer across languages and achieve high performance on downstream tasks. However, we questio…
Transfer LearningLiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs
Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. We present L…
Cross-Lingual TransferReducing language context confusion for end-to-end code-switching automatic speech recognition
Code-switching deals with alternative languages in communication process. Training end-to-end (E2E) automatic speech recognition (ASR) systems for code-switching is especially challenging as code-switching training data …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Sentencespeech-recognition+1