Papers with language translation
Complete Chess Games Enable LLM Become A Chess Master (2025.naacl-short)
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| Challenge: | Large language models (LLMs) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. |
| Approach: | They propose a Large language model that can play chess games by transforming a game into a textual format with the best move represented in the Forsyth-Edwards Notation. |
| Outcome: | The proposed model achieves professional-level Elo rating of 1788 in matches against the standard Elo-rated Stockfish when permitted to sample 10 times. |
PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text (2021.eacl-demos)
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| Challenge: | Prior punctuation restoration methods have focused on using lexical features, prosodic features or combination of both. |
| Approach: | They propose a multitask modeling approach to restore punctuation in multiple high resource languages using acoustic models and a computational model. |
| Outcome: | The proposed system can restore punctuation in Germanic, Romanic and low resource languages without extensive knowledge of grammar or syntax. |
Detect, Disambiguate, and Translate: On-Demand Visual Reasoning for Multimodal Machine Translation with Large Vision-Language Models (2025.naacl-long)
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Danyang Liu, Fanjie Kong, Xiaohang Sun, Dhruva Patil, Avijit Vajpayee, Zhu Liu, Vimal Bhat, Najmeh Sadoughi
| Challenge: | Multimodal machine translation (MMT) aims to leverage additional modalities beyond text . current MMT systems rely heavily on monolingual English captioning data . |
| Approach: | They propose a reasoning-based framework to leverage large-scale vision-language models for MMT . they propose Detect, Disambiguate, and Translate framework to detect ambiguity in input sentence . |
| Outcome: | The proposed framework outperforms state-of-the-art models in disambiguation accuracy and translation quality. |
Pipeline Signed Japanese Translation Focusing on a Post-positional Particle Complement and Conjugation in a Low-resource Setting (2021.findings-acl)
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| Challenge: | a pipeline translation method is proposed to take advantage of the similarities and differences between sign language and spoken language. |
| Approach: | They propose a pipeline translation method that takes advantage of similarities between spoken and spoken Japanese . they map glosses to spoken language words and train them using a monolingual Japanese corpus . |
| Outcome: | The proposed method performs robustly on the low-resource corpus and is +4.4/+4.9 points above baseline. |
LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)
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| Challenge: | Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. |
| Approach: | They propose to train different MMT models to support translations between different languages. |
| Outcome: | The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
Byte-based Multilingual NMT for Endangered Languages (2022.coling-1)
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| Challenge: | Existing work has not studied how byte encoding can benefit endangered languages . multilingual neural machine translation (MNMT) models suffer from out-of-vocabulary issues and representation bottleneck . |
| Approach: | They propose a multilingual multilingual neural machine translation system to alleviate the representation bottleneck and improve translation performance in endangered languages. |
| Outcome: | The proposed system outperforms subword-based models on twelve languages up to +18.5 BLEU points, an 840% relative improvement over baseline models. |
SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scale (2025.findings-acl)
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| Challenge: | Existing work on sign language video processing focuses on the face, hands and body posture of the signer. |
| Approach: | They propose to learn the handshapes and rich facial expressions of sign languages in a self-supervised fashion by learning from individual frames rather than video sequences. |
| Outcome: | The proposed model is more efficient than previous work on sign language pre-training. |
SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering (2026.acl-long)
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| Challenge: | Current safety alignment methods fail to identify intended benign task before refusing to respond. |
| Approach: | They propose a method that uses inference-time trajectory-shifting to guide model behavior . they show that LLMs persist in refusing inputs containing harmful content . |
| Outcome: | The proposed approach reduces over-refusals with minimal impact on utility. |