| Challenge: | In end-to-end e-commerce, the inclusion of a dedicated spelling correction model, and the augmentation of that model’s training data with language-relevant phenomena, each improve robustness and consistency of search results. |
| Approach: | They first analyze the spelling-robustness of a population of machine translation systems and then apply them to a multilingual e-commerce setting to test whether spelling variations affect MT output and user behavior. |
| Outcome: | The proposed model reduces the number of BPE operations and improves spelling-robustness in six languages. |
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| Challenge: | Existing studies have demonstrated that cross-lingual information retrieval performance is highly dependent on query translation quality. |
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Retrieval Augmented Spelling Correction for E-Commerce Applications (2024.emnlp-industry)
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| Challenge: | e-commerce spelling correction services face a challenge with new brand names . we propose a new approach that uses a fine-tuned retrieval algorithm to correct for brand names. |
| Approach: | They propose a method that uses product names to be incorporated into a large language model to do contextual spelling correction. |
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Search Query Spell Correction with Weak Supervision in E-commerce (2023.acl-industry)
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| Challenge: | Misspelled search queries can lead to empty or irrelevant products . only 29% of the population in india is proficient in english . |
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Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations (2025.emnlp-main)
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Yimin Xiao, Yongle Zhang, Dayeon Ki, Calvin Bao, Marianna J. Martindale, Charlotte Vaughn, Ge Gao, Marine Carpuat
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Grammatical Error Correction through Round-Trip Machine Translation (2023.findings-eacl)
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| Challenge: | A decade ago the idea of using round-trip MT to guide grammatical error correction was not feasible due to the low quality of MT systems of the day. |
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Did Translation Models Get More Robust Without Anyone Even Noticing? (2025.acl-long)
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| Challenge: | Neural machine translation models are highly sensitive to “noisy” inputs, such as spelling errors, abbreviations, and formatting issues. |
| Approach: | They revisit this insight in light of recent multilingual MT models and large language models applied to machine translation. |
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Improving Robustness of Machine Translation with Synthetic Noise (N19-1)
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| Challenge: | Recent work on MT robustness has demonstrated the need to build or adapt systems that are resilient to such noise. |
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Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)
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Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin
| Challenge: | Currently, traditional evaluation methods struggle to detect subtle translation errors. |
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Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks (2023.findings-emnlp)
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| Challenge: | BERTScore, BLEURT, and COMET are automatic evaluation metrics that are often underperformed on adversarially-synthesized texts. |
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