Papers with multilingual transfer
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach (2025.acl-industry)
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| Challenge: | Dual quality is a problem where products with identical ingredients or characteristics are sold under the same brand and similar packaging in different markets, but are significantly altered in composition or quality parameters. |
| Approach: | They propose to use natural language processing to detect inconsistent product quality by analyzing a Polish-language dataset and using different approaches. |
| Outcome: | The proposed approach can detect and address inconsistent product quality in Polish and other languages. |
Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks (2023.eacl-main)
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| Challenge: | Performance prediction for natural language processing (NLP) is based on a framework of Bayesian matrix factorisation . it avoids hyperparameter tuning and provides uncertainty estimates over predictions. |
| Approach: | They propose to use Bayesian matrix factorisation to predict the performance of language pairs depicted by grey cells. |
| Outcome: | The proposed framework outperforms the state-of-the-art in several NLP benchmarks, including machine translation and cross-lingual entity linking. |
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing (N19-1)
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| Challenge: | Existing methods for multilingual transfer are limited by their dynamic nature. |
| Approach: | They propose a method that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. |
| Outcome: | The proposed method outperforms the state-of-the-art on 6 languages, yielding an improvement of 6.8 LAS points on average. |
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations (2020.emnlp-main)
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| Challenge: | Recent studies consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks. |
| Approach: | They propose to fuse both views using canonical correlation analysis and use it to infer typological features and language phylogenies to construct a multi-view language vector space for multilingual machine translation. |
| Outcome: | The proposed model achieves competitive translation accuracy in multilingual machine translation tasks without expensive retraining of massive multilingual or ranking models. |
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents (2022.acl-long)
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| Challenge: | Document-level neural machine translation (DocNMT) is a powerful tool for integrating cross-sentence context into translations. |
| Approach: | They explore whether and how contextual modeling in DocNMT is transferable via multilingual modeling. |
| Outcome: | The proposed model can be used to transfer from teacher languages to student languages with no documents but sentence level data. |
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Multilingual data is more beneficial for NMT models that translate from the LRL to a target language than those that translate into the LLLs. |
| Approach: | They propose a decoder that embeds character n-grams into NMT models that translate from an LRL to a target language. |
| Outcome: | The proposed decoder improves the performance of NMT models that translate from an LRL to a target language. |
Adaptive Token-level Cross-lingual Feature Mixing for Multilingual Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Multilingual neural machine translation models can translate multiple language pairs in a single model but lacks ability to capture language-specific features. |
| Approach: | They propose a token-level feature mixing method that captures different features and dynamically determines feature sharing across languages. |
| Outcome: | The proposed method outperforms baselines and can be extended to zero-shot translation. |
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)
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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | 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. |
| Approach: | They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions. |
| Outcome: | The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. |
Sources of Transfer in Multilingual Named Entity Recognition (2020.acl-main)
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| Challenge: | naive training of named-entity recognition models using annotated data from multiple languages consistently underperforms monolingual models. |
| Approach: | They propose a polyglot named-entity recognition model where one model is trained using annotated data drawn from multiple languages. |
| Outcome: | The proposed model outperforms models trained on monolingual data despite more training data . the proposed model shares many parameters across languages and fine-tunes them to outperFORM monolingual models. |