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Found 11 papers, 2 papers with code
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models.
A Secure and Disambiguating Approach for Generative Linguistic Steganography
Firstly, we propose a secure token-selection principle that the sum of selected tokens' probabilities is positively correlated to statistical imperceptibility.
Multi-Task Neural Model for Agglutinative Language Translation
Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora.
Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
Experimental results show that our morphologically motivated word segmentation method is better suitable for the NMT model, which achieves significant improvements on Turkish-English and Uyghur-Chinese machine translation tasks on account of reducing data sparseness and language complexity.
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0. 5-0. 9 BLEU improvements)
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach.
A Bilingual Discourse Corpus and Its Applications
Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation.
