Papers with multilingual NLP
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus (2021.acl-demo)
Copied to clipboard
| Challenge: | 7000 languages worldwide are spoken, but most research is focused on English . multilinguality is essential for multilingual research, and is a key component of the process. |
| Approach: | They propose a wordaligned parallel corpus that can be browsed using an online tool . they use the word alignment tools SimAlign and BabelNet to find the alignments . |
| Outcome: | The proposed tool can be set up for any parallel corpus and explores its quality and properties. |
Multi-teacher Distillation for Multilingual Spelling Correction (2023.emnlp-industry)
Copied to clipboard
| Challenge: | a multilingual spelling correction model is needed to meet the tight latency requirements of multilingual NLP . a monolingual teacher model is trained for each language/locale, and individual models are distilled into a single student model . |
| Approach: | They propose a multilingual approach to spelling correction using multi-teacher distillation . they train a monolingual teacher model for each language and distill them into a single model . |
| Outcome: | The proposed model can meet the tight latency requirements of deployed services. |
Don’t Touch My Diacritics (2025.naacl-short)
Copied to clipboard
| Challenge: | a recent paper examines the effects of preprocessing text with diacritics on model performance . we show that inconsistent encoding of diacritized characters and removing diacritical characters can have detrimental downstream effects . |
| Approach: | They propose to improve the handling of diacritized text by preserving diacritics and removing them altogether. |
| Outcome: | The proposed approach reduces the number of errors in the preprocessing process, the authors argue . they show that the proposed approach can reduce the number and complexity of errors . |
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets (2024.findings-naacl)
Copied to clipboard
| Challenge: | a new study aims to assess linguistic diversity of multilingual data sets against a reference language sample . linguistic diversity is typically measured as the number of languages included in the data set . but such measures do not consider structural properties of the included languages . |
| Approach: | They propose to measure linguistic diversity against a reference language sample to maximise linguistic diversity. |
| Outcome: | The proposed measure can be used to identify the types of languages that are not represented in a data set. |
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Current methods for bilingual lexicon induction rely on the induction of cross-lingual word embeddings (CLWEs) such as VecMap or mPLMs are not available for multilingual NLP. |
| Approach: | They propose a semi-supervised post-hoc reranking method which combines cross-lingual lexical knowledge from multilingual pretrained language models with original CLWEs. |
| Outcome: | The proposed method outperforms existing methods on two standard benchmarks spanning a wide spectrum of languages and is robust to different CLWEs. |
What is ”Typological Diversity” in NLP? (2024.emnlp-main)
Copied to clipboard
| Challenge: | linguistic typology is commonly used to motivate language selections, but there are no set definitions or criteria for such claims. |
| Approach: | They propose to use linguistic typology to motivate language selections on the basis that a broad typological sample ought to imply generalization across a wide range of languages. |
| Outcome: | The proposed measures show that skewed language selection can lead to overestimated multilingual performance. |
Happiness is Sharing a Vocabulary: A Study of Transliteration Methods (2026.eacl-long)
Copied to clipboard
| Challenge: | a key problem in multilingual NLP is script barrier, which makes it difficult to share knowledge between languages . a new study shows that transliteration can be useful for languages using non-Latin scripts . |
| Approach: | They propose to use romanization, phonemic transcription, and substitution ciphers to evaluate models . romanization outperforms other input types in 7 out of 8 evaluation settings . |
| Outcome: | The proposed approach outperforms other input types on three tasks and is the most effective . romanization outperformed other input type in 7 out of 8 evaluation settings . |
A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems (2023.emnlp-main)
Copied to clipboard
Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Anna Korhonen, Ivan Vulić
| Challenge: | Existing systems trained for Arabic or Turkish using annotated data fully parallel to English ToD data still exhibit diminished ToD task performance. |
| Approach: | They define new quantitative measures of absolute and relative equivalence in system performance, capturing disparities across languages and within individual languages. |
| Outcome: | The proposed measures capture disparities across languages and within individual languages. |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to learn general language representations from large volumes of unlabeled text have been used to improve multilingual NLP. |
| Approach: | They propose to use a spatial arrangement method to generate large-scale evaluation datasets that balance cross-lingual alignment with language specificity. |
| Outcome: | The proposed method produces semantic verb classes and fine-grained similarity scores for nearly 130 thousand verb pairs. |
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on colexification patterns relies on annotated word lists, limiting scalability and usefulness in NLP. |
| Approach: | They propose two methods to train multilingual graphs from colexification patterns using an unannotated parallel corpus. |
| Outcome: | The proposed methods achieve high recall on CLICS and transfer learning in multilingual graphs. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
Copied to clipboard
Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |
BnMMLU: Measuring Massive Multitask Language Understanding in Bengali (2026.findings-acl)
Copied to clipboard
| Challenge: | Large-scale multitask benchmarks have driven rapid progress in language modeling, yet most emphasize low-resource languages like English. |
| Approach: | They propose a benchmark for massive multitask language understanding in Bengali . they use a dataset that preserves mathematical content via MathML and a subset of questions most frequently missed by top systems to stress difficult cases. |
| Outcome: | The proposed benchmark covers 24 model variants across 11 LLM families. |
On Bilingual Lexicon Induction with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to induction bilingual lexicons still require cross-lingual word representations . a recent study shows that few-shot prompting with in-context examples from nearest neighbours achieves the best performance . |
| Approach: | They examine whether it is possible to prompt and fine-tune multilingual LLMs for BLI . they experiment with 18 open-source text-to-text mLLMs of different sizes . |
| Outcome: | The proposed approach is compared with existing approaches on two standard BLI benchmarks covering a range of typologically diverse languages. |
Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models (2025.findings-acl)
Copied to clipboard
Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang, Lihan Feng, Annant Jain, Faadil Abdullah Shaikh, Jonibek Mansurov, Mohamed Fazli Mohamed Imam, Jesus-German Ortiz-Barajas, Rendi Chevi, Alham Fikri Aji
| Challenge: | Large Language Models excel in zero-shot and few-shot tasks, but their architecture makes them difficult to use. |
| Approach: | They adapt Large Language Models (LLMs) for zero-shot generalization using Statement Tuning . they find encoders can achieve zero- shot cross-lingual generalization . |
| Outcome: | The proposed model generalizes well across languages while being more efficient. |
Shared Path: Unraveling Memorization in Multilingual LLMs through Language Similarities (2025.emnlp-main)
Copied to clipboard
| Challenge: | Using multilingual models, we find that treating languages in isolation obscures the true patterns of memorization. |
| Approach: | They propose a graph-based correlation metric that incorporates language similarity to analyze cross-lingual memorization. |
| Outcome: | The proposed model incorporates language similarity to analyze cross-lingual memorization in 95 languages. |
CLaS-Bench: A Cross-Lingual Alignment and Steering Benchmark (2026.findings-acl)
Copied to clipboard
Daniil Gurgurov, Yusser Al Ghussin, Tanja Baeumel, Cheng-Ting Chou, Patrick Schramowski, Marius Mosbach, Josef Van Genabith, Simon Ostermann
| Challenge: | Understanding and controlling behavior of large language models (LLMs) is an important topic in multilingual NLP. |
| Approach: | They propose a lightweight parallel-question benchmark for evaluating language-forcing behavior in large language models across 32 languages. |
| Outcome: | The proposed benchmark measures language steering in 32 languages across 32 languages. |
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Cross-Lingual Transfer in Sense-Aware Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality. |
| Approach: | They challenge this assumption by analyzing polysemy disambiguation and lexical semantic change in 28 languages and using confounding factors to account for perceived advantages. |
| Outcome: | The proposed models and benchmarks are compared across 28 languages and show that multilingual training is neither necessary nor beneficial for effective transfer. |