| Challenge: | Neural machine translation (NMT) methods fail to capture discourse phenomena such as pronominal anaphora, lexical consistency, and document coherence as the input text exceeds a single sentence. |
| Approach: | They examine the effects of model scale, data scale, and sequence length on translation quality when model size is limited. |
| Outcome: | The proposed model scales and data scales are compared with the existing models and show that increasing sequence length improves translation quality when model size is limited. |
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Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies (2025.acl-long)
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| Challenge: | Existing scaling laws suggest augmenting model size and training data results in enhanced performance, but recent studies reveal deviations, particularly in large language models, where performance improvements decelerate—a phenomenon known as sub-scaling. |
| Approach: | They propose a sub-optimal scaling law that better predicts performance in sub-scaling regimes by examining data quality and training strategies. |
| Outcome: | The proposed scaling law better predicts performance in sub-scaling regimes, highlighting the importance of data quality and diversity. |
Data and Parameter Scaling Laws for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent work shows that supervised neural machine translation models scale like a power law with the amount of training data and number of non-embedding parameters in the model. |
| Approach: | They show that cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and number of non-embedding parameters in the model. |
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Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
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Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments (2022.findings-emnlp)
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| Challenge: | Neural scaling laws define a predictable relationship between a model’s parameter count and its performance after training in the form of a power law. |
| Approach: | They perform an empirical investigation of language understanding tasks and evaluate their results to determine whether scaling laws can be used to accelerate model development. |
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Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
| Outcome: | The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores. |
Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)
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Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu
| Challenge: | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. |
| Approach: | They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains . |
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Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
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| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
When and Why is Document-level Context Useful in Neural Machine Translation? (D19-65)
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| Challenge: | Recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. |
| Approach: | They extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document- level context in NMT. |
| Outcome: | The proposed model is not interpretable as utilizing the context, and a long context is not helpful for NMT. |
Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models can be used to evaluate long documents, but they are limited by context window limitations. |
| Approach: | They propose to use granularity-aligned prompting and Focus Sentence Prompting to improve evaluation. |
| Outcome: | a new study shows that long texts lead to fewer error spans and reduced system ranking accuracy. |
Addressing the Length Bias Challenge in Document-Level Neural Machine Translation (2023.findings-emnlp)
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| Challenge: | Document-level neural machine translation (DNMT) models incorporate context information through increased maximum lengths of source and target sentences. |
| Approach: | They propose a sliding decoding strategy that limits the length of target sentences . they propose 'length-normalized attention mechanism' to aid the model in focusing on target information . |
| Outcome: | The proposed method can achieve state-of-the-art results on open datasets. |