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Machine Translation
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Machine translation is the task of translating a sentence in a source language to a different target language.
Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.
One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/violet-zct/fairseq-detect-hallucination
공식 코드Detecting Hallucinated Content in Conditional Neural Sequence Generation
https://github.com/violet-zct/fairseq-dro-mnmt
공식 코드https://github.com/violet-zct/pytorch-reorder-nmt
공식 코드Handling Syntactic Divergence in Low-resource Machine Translation
https://github.com/vityavitalich/meritfed
공식 코드Low-Resource Machine Translation through the Lens of Personalized Federated Learning
https://github.com/vityavitalich/meritopt
공식 코드Low-Resource Machine Translation through the Lens of Personalized Federated Learning
https://github.com/vman049/MultilingualLexicalSemantics
공식 코드How do lexical semantics affect translation? An empirical study
https://github.com/voidmagic/parameter-differentiation
공식 코드Parameter Differentiation based Multilingual Neural Machine Translation
https://github.com/vrclc-duk/ml-en-transliteration
공식 코드Romanized to Native Malayalam Script Transliteration Using an Encoder-Decoder Framework
https://github.com/vyraun/dlp
공식 코드On Dimensional Linguistic Properties of the Word Embedding Space
https://github.com/vyraun/finding-memo
공식 코드Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks
https://github.com/vyraun/hallucinations
공식 코드The Curious Case of Hallucinations in Neural Machine Translation
https://github.com/vyraun/literalness
공식 코드https://github.com/vyraun/long-tailed
공식 코드https://github.com/wangchunliu/chinese-sbn-parsing
공식 코드https://github.com/wangchunliu/sbn-evaluation-tool
공식 코드https://github.com/wangclnlp/DeepSpeed-Chat-Extension/tree/main/examples/esrl
공식 코드ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
https://github.com/wangclnlp/csem
공식 코드Learning Evaluation Models from Large Language Models for Sequence Generation
https://github.com/wangke1996/translation-suggestion-psgd
공식 코드Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation
https://github.com/wangqi1996/kok
공식 코드Non-Parametric Online Learning from Human Feedback for Neural Machine Translation
https://github.com/wangqiangneu/dlcl
공식 코드https://github.com/wangyong1122/dtn
공식 코드Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
https://github.com/wanyu2018umac/Self-Paced-Learning-for-Neural-Machine-Translation
공식 코드https://github.com/wanyu2018umac/UniTE
공식 코드https://github.com/wasiahmad/sumgentobt
공식 코드Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
https://github.com/webspellchecker/unlp-2023-shared-task
공식 코드RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans
https://github.com/weichuanw/llm-based-mt-via-model-editing
공식 코드https://github.com/weijia-xu/hallucinations-in-nmt
공식 코드Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
https://github.com/weixiangyan/codetransocean
공식 코드CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
https://github.com/weiyali126/TACTIC
공식 코드TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
https://github.com/wellecks/mgs
공식 코드MLE-guided parameter search for task loss minimization in neural sequence modeling
https://github.com/wengsyx/low-resource-text-translation
공식 코드Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
https://github.com/wgantt/multimuc
공식 코드https://github.com/whaleloops/interpoetry
공식 코드https://github.com/whher/nmt-de-bar
공식 코드Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
https://github.com/wingseeye/transeval-lrm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.