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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/nickboucher/imperceptible
공식 코드https://github.com/nickdeas/masive
공식 코드MASIVE: Open-Ended Affective State Identification in English and Spanish
https://github.com/nickeilf/visual-cues-error-correction
공식 코드https://github.com/nicolae-teodor-pavel/commit-message-generation
공식 코드A Sketch-Based Neural Model for Generating Commit Messages from Diffs
https://github.com/nightingal3/idiom-translation
공식 코드https://github.com/nii-yamagishilab/xfever
공식 코드https://github.com/nikhil-iyer-97/wide-minima-density-hypothesis
공식 코드Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
https://github.com/nikitacs16/xlift_dst
공식 코드Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
https://github.com/ningningyang/NDPR
공식 코드Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
https://github.com/ningningyang/Transformer-GCRF
공식 코드Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
https://github.com/nitikam/tangled
공식 코드Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics
https://github.com/niutrans/niutensor
공식 코드https://github.com/niyatibafna/bli-for-indic-languages
공식 코드https://github.com/niyatibafna/pgns-for-lrmt
공식 코드Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!
https://github.com/nju-websoft/JAPE
공식 코드Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
https://github.com/njunlp/knn-box
공식 코드kNN-BOX: A Unified Framework for Nearest Neighbor Generation
https://github.com/njunlp/mmt-llm
공식 코드Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
https://github.com/njunlp/trans0
공식 코드https://github.com/nl2g/effeval
공식 코드EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics
https://github.com/nl2g/xcomet-lite
공식 코드xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics
https://github.com/nlp-dke/NMTGMinor/tree/master/recipes/zero-shot
공식 코드Improving Zero-Shot Translation by Disentangling Positional Information
https://github.com/nlp-waseda/kanbun-lm
공식 코드Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods by Language Models
https://github.com/nlp2ct/consisttl
공식 코드ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
https://github.com/nlp2ct/e-att
공식 코드https://github.com/nlp2ct/hil-mt
공식 코드Human-in-the-loop Machine Translation with Large Language Model
https://github.com/nlp2ct/unite
공식 코드https://github.com/nlp2ct/variance-aware-mt-test-sets
공식 코드https://github.com/nlpaueb/gr-nlp-toolkit
공식 코드https://github.com/nlpcode/mcmcxlnet
공식 코드Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
https://github.com/nlpcuom/quality-matters
공식 코드Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora
https://github.com/noe/sparsely_factored_nmt
공식 코드https://github.com/nolongerprejudice/tgbi
공식 코드On Measuring Gender Bias in Translation of Gender-neutral Pronouns
https://github.com/nttcslab-nlp/doc_lm
공식 코드https://github.com/nttcslab-nlp/simplifyingmt_acl24
공식 코드https://github.com/ntunlp/chatgpt_eval
공식 코드A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.