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Showing 6501–6550 of 12395 papers

TitleStatusHype
Hindi to English Transfer Based Machine Translation System—0
Hindi to English: Transformer-Based Neural Machine Translation—0
Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation—0
hinglishNorm - A Corpus of Hindi-English Code Mixed Sentences for Text Normalization—0
Hinglish to English Machine Translation using Multilingual Transformers—0
HIN: Hierarchical Inference Network for Document-Level Relation Extraction—0
Hint-based Training for Non-Autoregressive Translation—0
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints—0
HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction—0
HistDiST: Histopathological Diffusion-based Stain Transfer—0
HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features—0
HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation—0
Homograph Disambiguation Through Selective Diacritic Restoration—0
Homonymy and Polysemy Detection with Multilingual Information—0
Homoscatter: Towards efficient connectivity for ZigBee backscatter system—0
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages—0
How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus—0
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?—0
How Does Pretraining Improve Discourse-Aware Translation?—0
How do Humans Evaluate Machine Translation—0
How do LSPs compute MT discounts? Presenting a company’s pipeline and its use—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?—0
How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?—0
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?—0
How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task—0
How Generative Adversarial Networks and Their Variants Work: An Overview—0
How Good are Commercial Large Language Models on African Languages?—0
How good are Large Language Models on African Languages?—0
How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech—0
How Length Prediction Influence the Performance of Non-Autoregressive Translation?—0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures—0
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes—0
How much does a word weigh? Weighting word embeddings for word sense induction—0
How Much Does Tokenization Affect Neural Machine Translation?—0
How Multilingual Are Large Language Models Fine-Tuned for Translation?—0
How Old Are You? Face Age Translation with Identity Preservation Using GANs—0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?—0
How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?—0
How Sentiment Analysis Can Help Machine Translation—0
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus—0
How Synchronous are Adjuncts in Translation Data?—0
How to Account for Idiomatic German Support Verb Constructions in Statistical Machine Translation—0
How to Agree to Disagree: Managing Ontological Perspectives using Standpoint Logic—0
How to Ask Good Questions? Try to Leverage Paraphrases—0
How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation—0
How to configure statistical machine translation with linked open data resources—0
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation—0
How to Know the Best Machine Translation System in Advance before Translating a Sentence?—0
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise on Machine Translation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1T5BLEU-134.07—Unverified
2LongformerBLEU-122.74—Unverified
#ModelMetricClaimedVerifiedStatus
1ALMA-Gemma-7B-IT-STBLEU56.21—Unverified
2ALMA-Gemma-7B-ITBLEU52.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CFEAR-3-s50translation error [%]1.09—Unverified