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# Who cites «COMET: A Neural Framework for MT Evaluation» · page 5

1167 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/rei-2020-comet-neural-framework-mt-evaluation/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-7/) · [8](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-8/) · [9](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-9/) · [10](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-10/) · [11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-11/) · [12](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rei-2020-comet-neural-framework-mt-evaluation-12/)

1. [Findings of the WMT 2024 Biomedical Translation Shared Task: Test Sets on Abstract Level](https://aclanthology.org/2024.wmt-1.6/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
2. [Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems](https://aclanthology.org/2024.wmt-1.57/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
3. [Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains](https://aclanthology.org/2024.acl-short.45/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
4. [Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?](https://aclanthology.org/2024.emnlp-main.24/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
5. [Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation](https://aclanthology.org/2024.emnlp-main.949/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
6. [From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation](https://arxiv.org/abs/2403.14118) *— 2024 · arXiv:2403.14118 · 2024 International Joint Conference on Neural Networks (IJCNN)*
7. [From LLM to NMT: Advancing Low-Resource Machine Translation with Claude](https://arxiv.org/abs/2404.13813) *— 2024 · arXiv:2404.13813*
8. [G-DIG: Towards Gradient-based DIverse and hiGh-quality Instruction Data Selection for Machine Translation](https://aclanthology.org/2024.acl-long.821/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [GAATME: A Genetic Algorithm for Adversarial Translation Metrics Evaluation](https://aclanthology.org/2024.lrec-main.668/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
10. [GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levels](https://arxiv.org/abs/2407.03658) *— 2024 · arXiv:2407.03658*
11. [Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://aclanthology.org/2024.dmr-1.17/) *— 2024 · Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024*
12. [Gender-specific Machine Translation with Large Language Models](https://aclanthology.org/2024.mrl-1.10/) *— 2024 · Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)*
13. [Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!](https://aclanthology.org/2024.acl-long.856/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation](https://aclanthology.org/2024.amta-research.9/) *— 2024 · Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
15. [Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations](https://aclanthology.org/2024.findings-naacl.265/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
16. [HW-TSC 2024 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2024.wmt-1.39/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
17. [High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models](https://aclanthology.org/2024.findings-eacl.98/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
18. [How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data](https://aclanthology.org/2024.findings-acl.896/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
19. [How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?](https://aclanthology.org/2024.acl-short.58/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
20. [How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes](https://aclanthology.org/2024.amta-research.20/) *— 2024 · Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*
21. [INCLURE: a Dataset and Toolkit for Inclusive French Translation](https://aclanthology.org/2024.bucc-1.7/) *— 2024 · Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024*
22. [Implementing Gender-Inclusivity in MT Output using Automatic Post-Editing with LLMs](https://aclanthology.org/2024.eamt-1.48/) *— 2024 · Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)*
23. [Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment](https://aclanthology.org/2024.naacl-long.445/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
24. [Improving Minimum Bayes Risk Decoding with Multi-Prompt](https://aclanthology.org/2024.emnlp-main.1255/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
25. [Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data](https://aclanthology.org/2024.wmt-1.126/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
26. [Introduction au numéro spécial - Robustesse et limites des modèles de traitement automatique des langues Introduction to the special issue on robustness and limits of NLP models](https://aclanthology.org/2023.tal-2.1/) *— 2024*
27. [Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study](https://aclanthology.org/2024.sigul-1.20/) *— 2024 · Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024*
28. [Is Context Helpful for Chat Translation Evaluation?](https://arxiv.org/abs/2403.08314) *— 2024 · arXiv:2403.08314*
29. [Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://aclanthology.org/2024.emnlp-main.427/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
30. [Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis](https://aclanthology.org/2024.wmt-1.127/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
31. [Iterative Translation Refinement with Large Language Models](https://aclanthology.org/2024.eamt-1.17/) *— 2024 · Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)*
32. [JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus](https://aclanthology.org/2024.lrec-main.826/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
33. [KpopMT: Translation Dataset with Terminology for Kpop Fandom](https://aclanthology.org/2024.loresmt-1.3/) *— 2024 · Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)*
34. [Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages](https://aclanthology.org/2024.naacl-long.170/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
35. [LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models](https://aclanthology.org/2024.eacl-long.8/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback](https://aclanthology.org/2024.findings-naacl.92/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
37. [LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback](https://aclanthology.org/2024.findings-acl.488/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
38. [LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations](https://arxiv.org/abs/2410.02707) *— 2024 · arXiv:2410.02707*
39. [LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages](https://aclanthology.org/2024.findings-emnlp.631/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
40. [Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level](https://aclanthology.org/2024.emnlp-main.860/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
41. [Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning](https://aclanthology.org/2024.findings-emnlp.328/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
42. [Learning Personalized Alignment for Evaluating Open-ended Text Generation](https://aclanthology.org/2024.emnlp-main.737/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
43. [Learning from others'' mistakes: Finetuning machine translation models with span-level error annotations](https://arxiv.org/abs/2410.16509) *— 2024 · arXiv:2410.16509*
44. [Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://aclanthology.org/2024.emnlp-main.896/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
45. [Linear-time Minimum Bayes Risk Decoding with Reference Aggregation](https://aclanthology.org/2024.acl-short.71/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
46. [Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation](https://md.mova.elamur.ai/free/en/docs/papers/wein-2024-lost-translationese-reducing-translation-effect/) *— our card · 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation](https://aclanthology.org/2024.findings-acl.211/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
48. [M5 – A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks](https://aclanthology.org/2024.findings-emnlp.250/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
49. [MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation](https://aclanthology.org/2024.naacl-long.358/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
50. [MT-Ranker: Reference-free machine translation evaluation by inter-system ranking](https://arxiv.org/abs/2401.17099) *— 2024 · arXiv:2401.17099*
51. [Machine Translation Meets Large Language Models: Evaluating ChatGPT''s Ability to Automatically Post-Edit Literary Texts](https://aclanthology.org/2024.ctt-1.7/) *— 2024 · Proceedings of the 1st Workshop on Creative-text Translation and Technology*
52. [Measuring Uncertainty in Neural Machine Translation with Similarity-Sensitive Entropy](https://aclanthology.org/2024.eacl-long.129/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration](https://aclanthology.org/2024.wmt-1.32/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
54. [MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task](https://aclanthology.org/2024.wmt-1.35/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
55. [Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation](https://aclanthology.org/2024.emnlp-main.803/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
56. [Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean](https://aclanthology.org/2024.lrec-main.1024/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
57. [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://arxiv.org/abs/2404.04925) *— 2024 · arXiv:2404.04925*
58. [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://aclanthology.org/2024.findings-naacl.176/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
59. [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://aclanthology.org/2024.acl-long.749/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles](https://aclanthology.org/2024.findings-naacl.33/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
61. [Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies](https://aclanthology.org/2024.acl-long.110/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Neuron Specialization: Leveraging Intrinsic Task Modularity for Multilingual Machine Translation](https://aclanthology.org/2024.emnlp-main.374/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
63. [Non-Exchangeable Conformal Language Generation with Nearest Neighbors](https://aclanthology.org/2024.findings-eacl.129/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
64. [Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References](https://aclanthology.org/2024.naacl-long.367/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
65. [On Instruction-Finetuning Neural Machine Translation Models](https://aclanthology.org/2024.wmt-1.114/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
66. [On Measuring Context Utilization in Document-Level MT Systems](https://aclanthology.org/2024.findings-eacl.113/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
67. [On the True Distribution Approximation of Minimum Bayes-Risk Decoding](https://aclanthology.org/2024.naacl-short.38/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
68. [Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows](https://aclanthology.org/2024.wmt-1.101/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
69. [Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach](https://aclanthology.org/2024.emnlp-main.708/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
70. [POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation](https://aclanthology.org/2024.acl-long.537/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
71. [Patentformer: A Novel Method to Automate the Generation of Patent Applications](https://aclanthology.org/2024.emnlp-industry.101/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track*
72. [Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model](https://aclanthology.org/2024.findings-acl.821/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
73. [Pitfalls and Outlooks in Using COMET](https://aclanthology.org/2024.wmt-1.121/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
74. [Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model](https://aclanthology.org/2024.findings-acl.722/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
75. [Pretraining Language Models Using Translationese](https://aclanthology.org/2024.emnlp-main.334/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
76. [Prompting Large Language Models for Idiomatic Translation](https://aclanthology.org/2024.ctt-1.4/) *— 2024 · Proceedings of the 1st Workshop on Creative-text Translation and Technology*
77. [Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation](https://aclanthology.org/2024.eamt-1.54/) *— 2024 · Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)*
78. [PyMarian: Fast Neural Machine Translation and Evaluation in Python](https://aclanthology.org/2024.emnlp-demo.34/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
79. [Quality and Quantity of Machine Translation References for Automatic Metrics](https://aclanthology.org/2024.humeval-1.1/) *— 2024 · Proceedings of the Fourth Workshop on Human Evaluation of NLP Systems (HumEval) @ LREC-COLING 2024*
80. [Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model](https://aclanthology.org/2024.acl-long.836/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
81. [REFeREE: A REference-FREE Model-Based Metric for Text Simplification](https://aclanthology.org/2024.lrec-main.1200/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
82. [Recovering document annotations for sentence-level bitext](https://aclanthology.org/2024.findings-acl.589/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
83. [RepEval: Effective Text Evaluation with LLM Representation](https://aclanthology.org/2024.emnlp-main.398/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
84. [Reranking Laws for Language Generation: A Communication-Theoretic Perspective](https://arxiv.org/abs/2409.07131) *— 2024 · arXiv:2409.07131*
85. [Revisiting Demonstration Selection Strategies in In-Context Learning](https://aclanthology.org/2024.acl-long.492/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
86. [SCALE: Synergized Collaboration of Asymmetric Language Translation Engines](https://aclanthology.org/2024.findings-acl.941/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
87. [Self-Modifying State Modeling for Simultaneous Machine Translation](https://aclanthology.org/2024.acl-long.528/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing](https://aclanthology.org/2024.findings-emnlp.3/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
89. [Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs](https://aclanthology.org/2024.findings-acl.506/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
90. [Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models](https://aclanthology.org/2024.acl-long.567/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
91. [Simul-MuST-C: Simultaneous Multilingual Speech Translation Corpus Using Large Language Model](https://aclanthology.org/2024.emnlp-main.1238/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
92. [Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair](https://aclanthology.org/2024.emnlp-main.1248/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
93. [Simultaneous Machine Translation with Large Language Models](https://aclanthology.org/2024.alta-1.7/) *— 2024 · Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association*
94. [Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling](https://arxiv.org/abs/2410.11325) *— 2024 · arXiv:2410.11325*
95. [SpeechQE: Estimating the Quality of Direct Speech Translation](https://aclanthology.org/2024.emnlp-main.1218/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
96. [Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting](https://aclanthology.org/2024.lrec-main.1362/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
97. [Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer](https://aclanthology.org/2024.findings-naacl.210/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
98. [The Effects of Pretraining in Video-Guided Machine Translation](https://aclanthology.org/2024.lrec-main.1380/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
99. [The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs](https://aclanthology.org/2024.gebnlp-1.7/) *— 2024 · Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)*
100. [Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability](https://aclanthology.org/2024.emnlp-main.891/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*

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