Papers with manual translation
SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026.acl-long)
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Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha, Mihai Christodorescu, Kirill Levchenko, Varun Chandrasekaran
| Challenge: | Large language models (LLMs) have shown promise in producing idiomatic translations, but offer no correctness guarantees. |
| Approach: | They propose a C-to-Rust translation tool that uses an initial "unidiomatic" translation followed by an "idiomatic refinement" they evaluate SACTOR on 200 programs from two datasets and two more complex scenarios . |
| Outcome: | The proposed tool delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7 fewer Clippy warnings. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
Active Learning for Multilingual Semantic Parser (2023.findings-eacl)
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| Challenge: | Existing multilingual semantic parsing datasets are limited in translation effort due to data imbalance. |
| Approach: | They propose a first active learning procedure for multilingual semantic parsing (AL-MSP) it selects only a subset from existing datasets to be translated, they propose . |
| Outcome: | The proposed method significantly reduces translation costs with ideal selection methods. |
Neural Machine Translation for Low-Resourced Indian Languages (2020.lrec-1)
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| Challenge: | Neural machine translation (NMT) is an effective way to convert text to a different language without human involvement. |
| Approach: | They propose to use multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient machine translation system. |
| Outcome: | The proposed system outperforms Google translator and the existing translators on two of the most morphological rich Indian languages. |
MEDLINE as a Parallel Corpus: a Survey to Gain Insight on French-, Spanish- and Portuguese-speaking Authors’ Abstract Writing Practice (2020.lrec-1)
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| Challenge: | Existing corpora are used to train and evaluate machine translation systems, but little information is available about the methods used for producing the corpus, including translation direction. |
| Approach: | They used PubMed and publisher websites to obtain contact information for MEDLINE authors and asked about their abstract writing practices. |
| Outcome: | The authors of MEDLINE articles included in the English/Spanish, English/FR, and English/Portuguese (EN/PT) WMT 2019 test sets reported a response rate of over 20% . |
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
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Kazuki Tani, Ryoya Yuasa, Kazuki Takikawa, Akihiro Tamura, Tomoyuki Kajiwara, Takashi Ninomiya, Tsuneo Kato
| Challenge: | Existing test datasets for MLCC-MT have three problems: A source language sentence and its simplified target language sentence are not necessarily exactly parallel. |
| Approach: | They propose to use a test dataset to evaluate multi-level complexity-controllable machine translation (MLCC-MT) their results are compared to a standard test dataset constructed from the Newsela corpus . |
| Outcome: | The proposed test dataset is based on the Newsela corpus and is released . it includes automatic filtering, manual check for parallel target language sentences . |
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output (2021.emnlp-main)
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| Challenge: | Post-editing (PE) machine translation (MT) output can save time and reduce errors. |
| Approach: | They propose to use automatic word-level quality estimation to predict correctness of MT output to flag problematic output. |
| Outcome: | The proposed model is not good enough to support human translations, but is based on a visualization reflecting uncertainty of the model. |