Papers with English to
Sampling and Filtering of Neural Machine Translation Distillation Data (2021.naacl-srw)
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| Challenge: | In most of neural machine translation distillation or stealing scenarios, the highest-scoring hypothesis of the target model is used to train a new model. |
| Approach: | They propose to use the highest-scoring hypothesis of the target model (teacher) to train a new model (student). |
| Outcome: | The proposed method improves the performance of MT models in English to Czech and with reference translations. |
On the effective transfer of knowledge from English to Hindi Wikipedia (2025.coling-industry)
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| Challenge: | Existing studies show that in low-resource languages, Wikipedia articles on the same topic differ significantly due to cultural relevance and the varied expertise of contributors. |
| Approach: | They propose a lightweight framework to enhance knowledge equity between English and Hindi Wikipedia sections by extracting relevant information from external resources readily available. |
| Outcome: | The proposed framework enhances Hindi Wikipedia articles by 65% and 62% based on automatic and human judgment-based evaluations. |
Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints (2022.emnlp-industry)
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| Challenge: | We have deployed reliable and precise large-scale machine translation systems for several Indian regional languages. |
| Approach: | They develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. |
| Outcome: | The proposed model improves over iterations for English to Hindi and for other languages. |
Can Automatic Post-Editing Improve NMT? (2020.emnlp-main)
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| Challenge: | APE has been successful with statistical machine translation systems but has not been as successful over neural machine translation (NMT) systems. |
| Approach: | They propose to train neural APE models on a corpus of human post-edits of NMT and compile a larger corpus to test their hypothesis. |
| Outcome: | The proposed model can improve a strong in-domain NMT system, challenging the current understanding in the field. |
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model (2021.naacl-main)
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| Challenge: | Neural machine translation models that incorporate inter-sentential contexts can be trained only in document-level parallel data with sentential alignments. |
| Approach: | They propose a method to perform context-aware decoding with any pre-trained translation model . their method uses sentence-level parallel data and target-side document-level monolingual data . |
| Outcome: | The proposed method performs context-aware decoding on English to Russian translation using BLEU and contrastive tests. |
Literality and cognitive effort: Japanese and Spanish (L18-1)
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| Challenge: | pause-word ratios are indicators of cognitive effort during different translation modalities. |
| Approach: | They propose a notion of pause-word ratio computed using ranges of a pause length rather than lower cutoffs for pauses . they compare translation and post-editing for language pairs that are different in terms of semantic and syntactic remoteness . |
| Outcome: | The proposed pause-word ratio measures cognitive effort in translation and post-editing for language pairs that are different in terms of semantic and syntactic remoteness. |
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation (2022.emnlp-main)
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| Challenge: | generative multilingual models fine-tuned on English forget to generate non-English data when labeled data is only available in English . generative models fine tuned on English fail to generate multilingual summarization tasks when labeling data is available in other languages . |
| Approach: | They propose to use prompt tuning to overcome catastrophic forgetting in a generative task in . they assume a strict setting with no parallel data or machine translation . |
| Outcome: | The proposed method can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. |
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages (2023.acl-long)
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Ananya Sai B, Tanay Dixit, Vignesh Nagarajan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre
| Challenge: | Recent studies on machine translation systems focus on high-resource languages, but focus has shifted to low-resourced languages. |
| Approach: | They evaluate 16 metrics from a multidimensional quality metric dataset . they show pre-trained metrics have higher correlations with annotator scores . |
| Outcome: | The proposed evaluations show that pre-trained metrics outperform COMET on Indian languages. |
Strengthening the WiC: New Polysemy Dataset in Hindi and Lack of Cross Lingual Transfer (2024.lrec-main)
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| Challenge: | a new study addresses the problem of natural language processing in low-resource languages such as Hindi . the paper focuses on Word Sense Disambiguation, a fundamental NLP task that deals with polysemous words. |
| Approach: | They propose a Hindi WSD dataset that allows training and testing of contextualized models. |
| Outcome: | The proposed dataset enables training and testing of contextualized models in Hindi . the results show that the proposed dataset can handle polysemy tasks in low-resource languages . |