Papers with n-gram-based metrics
Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation (2025.naacl-srw)
Copied to clipboard
| Challenge: | Large language models excel in machine translation, but most studies focus on sentence-level translation. |
| Approach: | They propose to use LLMs as a judge paradigm to evaluate document-level translations by directly prompting them to translate entire documents in a single pass. |
| Outcome: | The proposed method improves translation quality even without document-level fine-tuning compared to translating sentences separately . |
Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing non-AutoRegressive (NAR) text generation models lack proper pre-training, making them far behind pre-trained autoregressive models. |
| Approach: | They propose a novel pre-training task to promote prediction consistency in non-autoregressive (NAR) generation. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves 17 times speedup in throughput. |
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore (2021.acl-short)
Copied to clipboard
| Challenge: | Neural machine translation models are biased toward limited translation references . BERTScore is a scoring function based on contextual embeddings that overcomes the limitations of n-gram-based metrics. |
| Approach: | They propose to fine-tune models with a new evaluation metric based on contextual embeddings to overcome the limitations of n-gram-based metrics. |
| Outcome: | The proposed training objective improves translations that are different from the translations but close in the contextual embedding space. |
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training (2023.acl-long)
Copied to clipboard
| Challenge: | Automated metrics are used for machine translation, but they are often considered to be black boxes with potential biases that are difficult to detect. |
| Approach: | They analyze automatic metrics from the perspective of their guidance for machine translation training. |
| Outcome: | The proposed measures improve the performance of machine translation models. |
Towards Multiple References Era – Addressing Data Leakage and Limited Reference Diversity in Machine Translation Evaluation (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent research shows a weak correlation between n-gram-based metrics and human evaluations in machine translation tasks. |
| Approach: | They propose to use multiple references generated by LLMs to improve alignment between automatic metrics and human evaluations. |
| Outcome: | The proposed approach improves the alignment between automatic metrics and human evaluations on the WMT22 benchmark with 4 languages and achieves a maximum accuracy gain of 9.5%. |
Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization (2025.acl-long)
Copied to clipboard
| Challenge: | n-gram-based metrics are considered indicative (even if imperfect) of human evaluation for English, but their suitability for other languages remains unclear. |
| Approach: | They systematically assess evaluation metrics for generation for languages and tasks using n-gram-based and neural-based metrics. |
| Outcome: | The proposed evaluation suite is based on eight languages from four typological families and shows that it is sensitivity to the language type at hand. |
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)
Copied to clipboard
Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges. |
| Approach: | They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments . |
| Outcome: | The proposed framework and evaluator are competitive in counter-argument generation tasks. |
LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing Question Answering systems are limited by noisy documents and flawed QA pairs. |
| Approach: | They propose a high-quality subset of NarrativeQA focused on literary works . they identify and correct low-quality QA samples while removing extraneous text . |
| Outcome: | The proposed subset of NarrativeQA is based on literary works. |