| Challenge: | varying task definitions and data conditions make it difficult to draw a meaningful comparison. |
| Approach: | They propose to use language identification to perform data filtering on MT data based on cross-lingual word embeddings to identify weaknesses in language identification tool. |
| Outcome: | The proposed methods perform well on three real-life, high resource MT tasks while performing weakly within more realistic task conditions. |
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Filtering and Mining Parallel Data in a Joint Multilingual Space (P18-2)
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| Challenge: | Using a cosine distance in a joint multilingual sentence embedding, we filter out noisy parallel data and mine for bitexts in large news collections. |
| Approach: | They propose to learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large monolingual texts. |
| Outcome: | The proposed approach improves a competitive baseline on the WMT'14 task by 0.3 BLEU by filtering out 25% of the training data. |
Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
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| Challenge: | Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective. |
| Approach: | They propose to use a stronger machine translation system to mitigat mismatch between training on original text and running inference on machine translated text. |
| Outcome: | The proposed approach is highly task dependent and calls into question the dominance of multilingual models for cross-lingual classification. |
Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
| Outcome: | The proposed model can be leveraged in cross-lingual tasks without parallel data . the proposed model is based on the World Atlas of Language Structures (WALS) and two extrinsic tasks . |
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
| Approach: | They propose a multilingual word embedding corpus which is acquired by neural machine translation and is based on monolingual data. |
| Outcome: | The proposed method is competitive with existing methods but on the cross-lingual document classification task, it obtains the best figures. |
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation (2020.acl-main)
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| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
| Approach: | They use a low-resource use-case and a high-resourced language pair to test different backtranslation scenarios and employ data selection to optimise the synthetic corpora. |
| Outcome: | The proposed method reduces the amount of data used while maintaining high-quality MT systems. |
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
| Approach: | They propose to use character-based embedding models to combine different embeddable models . they emphasize how character-driven embeddance models outperform other models on quality . |
| Outcome: | The proposed model outperforms other models on quality of extracted bilingual lexicons . comparable corpora are an interesting and practical alternative to parallel corporation . |
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)
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| Challenge: | a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely . |
| Approach: | They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair. |
| Outcome: | The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models . |
Multilingual Data Filtering using Synthetic Data from Large Language Models (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that effective filters can be created by utilising Large Language Models to synthetically label data, which is then used to train smaller neural models for filtering purposes. |
| Approach: | They extend this approach to languages beyond English to train neural models for filtering purposes. |
| Outcome: | The proposed approach is effective at filtering parallel text for translation quality and filtering for domain specificity. |
On the Role of Parallel Data in Cross-lingual Transfer Learning (2023.findings-acl)
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| Challenge: | Existing multilingual models do not exploit the full potential of monolingual data, a new study finds . prior work has shown that parallel data is beneficial for cross-lingual learning, but it is unclear if it is the data itself or the modeling of parallel interactions that matters. |
| Approach: | They compare unsupervised machine translation to supervised machine translator and gold parallel data to generate synthetic parallel data. |
| Outcome: | The proposed model generated parallel data is better than supervised machine translation and gold parallel data in both general and task-specific settings. |
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples (2025.findings-emnlp)
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| Challenge: | Cross-Lingual Semantic Discrimination (CLSD) is a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Approach: | They propose a lightweight task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Outcome: | The proposed task requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |