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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/rsennrich/nematus
공식 코드https://github.com/rsennrich/subword-nmt
공식 코드https://github.com/rsennrich/wmt16-scripts
공식 코드https://github.com/ruanchaves/hashformers
공식 코드Zero-shot hashtag segmentation for multilingual sentiment analysis
https://github.com/ruathudo/post-ocr-correction
공식 코드An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish
https://github.com/rucaibox/div-ref
공식 코드Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
https://github.com/rucaibox/para-ref
공식 코드Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
https://github.com/ruizgao/knnmt-meta-optimizer
공식 코드Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer
https://github.com/rutilel/tecs-a-dataset-and-benchmark-for-tense-consistency
공식 코드TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
https://github.com/rycolab/differentiable-subset-pruning
공식 코드https://github.com/rycolab/differential-subset-pruning
공식 코드https://github.com/rycolab/uid-decoding
공식 코드https://github.com/rycolab/wpda
공식 코드https://github.com/s-nlp/multilingual-transformer-detoxification
공식 코드SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text Detoxification
https://github.com/sInceraSs/ELSA
공식 코드Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
https://github.com/sIncerass/powernorm
공식 코드https://github.com/sacmehta/delight
공식 코드DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
https://github.com/saifulhaq95/indicirsuite
공식 코드IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages
https://github.com/salesforce/cove
공식 코드https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/salesforce/nonauto-nmt
공식 코드https://github.com/samakos/document-ai-
공식 코드https://github.com/sameenmaruf/Bi-MSMT
공식 코드Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
https://github.com/sameenmaruf/acl2020-contextnmt-cataphora
공식 코드Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
https://github.com/sameenmaruf/selective-attn
공식 코드Selective Attention for Context-aware Neural Machine Translation
https://github.com/samsung/mt-llm-nlu
공식 코드Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages
https://github.com/samuelbroscheit/entity_knowledge_in_bert
공식 코드Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
https://github.com/samuki/reinforce-joey
공식 코드Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
https://github.com/sapienzanlp/guardians-mt-eval
공식 코드Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!
https://github.com/sapienzanlp/human-parity-mt-eval
공식 코드https://github.com/sara-rezaeimanesh/idiom_translation
공식 코드A Comparative Study of LLMs, NMT Models, and Their Combination in Persian-English Idiom Translation
https://github.com/satwik77/Transformer-Computation-Analysis
공식 코드On the Computational Power of Transformers and its Implications in Sequence Modeling
https://github.com/sayeh1994/medical-corpus-semantic-similarity-evaluation
공식 코드https://github.com/scofield7419/ummt-vsh
공식 코드https://github.com/scotthlee/nrc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.