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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/amazon-research/contrastive-controlled-mt
공식 코드CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
https://github.com/amazon-research/gendered-profession-translations
공식 코드Faithful Target Attribute Prediction in Neural Machine Translation
https://github.com/amazon-research/idiom-mt
공식 코드Automatic Evaluation and Analysis of Idioms in Neural Machine Translation
https://github.com/amazon-research/machine-translation-gender-eval
공식 코드https://github.com/amazon-research/multiatis
공식 코드End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
https://github.com/amazon-science/bio-mqm-dataset
공식 코드Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains
https://github.com/amazon-science/contextual-product-qa
공식 코드xPQA: Cross-Lingual Product Question Answering across 12 Languages
https://github.com/amazon-science/doc-mt-metrics
공식 코드https://github.com/amazon-science/idioms-incontext-mt
공식 코드The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
https://github.com/amazon-science/m3t-multi-modal-translation-bench
공식 코드M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
https://github.com/amazon-science/multi-way-parallel-ccmatrix
공식 코드A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
https://github.com/ameenali/videomatch
공식 코드https://github.com/amirabaskohi/peach
공식 코드https://github.com/amirsartipi13/translated-english-benchmarks-to-persian
공식 코드https://github.com/amom-nar/amom
공식 코드AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
https://github.com/amujika/Fast-Slow-LSTM
공식 코드https://github.com/andreasgrv/unargmaxable
공식 코드Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice
https://github.com/andreeaiana/xmind
공식 코드MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
https://github.com/andreichertkov/tranfighterpro
공식 코드Translate your gibberish: black-box adversarial attack on machine translation systems
https://github.com/anonno2/crosstalk-generation
공식 코드Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
https://github.com/anonymousturtle433/anonymized-code
공식 코드https://github.com/anoopkunchukuttan/geomm
공식 코드Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
https://github.com/antoniogois/keystrokes_ape
공식 코드Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
https://github.com/antoniosudoso/attention-nilm
공식 코드Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/antot/human_parity_eamt2020
공식 코드Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019
https://github.com/antot/human_parity_mt
공식 코드Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation
https://github.com/antot/mt_lit_transformer
공식 코드https://github.com/antot/neural_vs_-phrasebased_smt_eacl17
공식 코드https://github.com/antot/pemt_creativity_reading
공식 코드The Impact of Post-editing and Machine Translation on Creativity and Reading Experience
https://github.com/antot/posteditese_mtsummit19
공식 코드https://github.com/anzeyimana/kinmt_naacl2024
공식 코드Low-resource neural machine translation with morphological modeling
https://github.com/aoncevay/multiview-langrep
공식 코드https://github.com/apeterswu/Depth_Growing_NMT
공식 코드https://github.com/apeterswu/RL4NMT
공식 코드A Study of Reinforcement Learning for Neural Machine Translation
https://github.com/apple/ml-behavioral-testing-for-mt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.