Papers with context utilization
On Measuring Context Utilization in Document-Level MT Systems (2024.findings-eacl)
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| Challenge: | Current studies on document-level translation evaluation focus on sentence-level models which are inadequate for capturing improvements in discourse phenomena. |
| Approach: | They propose to complement accuracy-based evaluation with measures of context utilization. |
| Outcome: | The proposed model can be used to handle context-dependent discourse phenomena using an automatic annotation tool. |
On Context Utilization in Summarization with Large Language Models (2024.acl-long)
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| Challenge: | Large language models excel in abstractive summarization tasks, delivering fluent and pertinent summaries. |
| Approach: | They conduct the first comprehensive study on context utilization and position bias in summarization. |
| Outcome: | The proposed benchmark compares two methods to alleviate position bias in summarization tasks. |
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025.findings-naacl)
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Md. Motahar Mahtab, Faisal Ahamed Khan, Md. Ekramul Islam, Md. Shahad Mahmud Chowdhury, Labib Imam Chowdhury, Sadia Afrin, Hazrat Ali, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin
| Challenge: | In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets. |
| Approach: | They propose to use Bangla as a language to create the most extensive human-annotated and validated Bangla NLP dataset. |
| Outcome: | The proposed method outperforms existing methods on Bangla NER datasets and performs competitively on English datasets. |
Analyzing Context Contributions in LLM-based Machine Translation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have achieved state-of-the-art performance in machine translation . however, the mechanisms by which LLMs use different parts of the input context remain unexplored . |
| Approach: | They propose to analyze how large language models use different parts of the input context . they highlight several key findings: the source part of few-shot examples contributes more than its corresponding targets . |
| Outcome: | The proposed model can leverage in-context learning to perform translation tasks without training . the proposed model is able to perform tasks without being explicitly trained for them . |
You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models (2025.emnlp-main)
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| Challenge: | Using sparse contextually rich examples, we demonstrate a strong association between training data sparsity and model performance. |
| Approach: | They propose two training strategies to leverage contextually rich examples in training data . they demonstrate strong association between sparsity and model performance . |
| Outcome: | The proposed training strategies improve translation accuracy by 6 and 8 percentage points on the ctxPro evaluation. |