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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/hyperbolic-embeddings
공식 코드Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
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/mbxp-exec-eval
공식 코드https://github.com/amazon-research/multiatis
공식 코드End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
https://github.com/amazon-research/stil-mbart-multiatis
공식 코드https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
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/idioms-incontext-mt
공식 코드The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
https://github.com/amazon-science/iwslt-autodub-task
공식 코드Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing
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/stac-speech-translation
공식 코드End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
https://github.com/ameenali/videomatch
공식 코드https://github.com/amikael/HELFI
공식 코드HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment
https://github.com/amilbhagat/Conditional-Consistency-Models
공식 코드Conditional Consistency Guided Image Translation and Enhancement
https://github.com/amirabaskohi/peach
공식 코드https://github.com/amirbar/speech2gesture
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/anastasiia-kornilova/MMDF
공식 코드https://github.com/anavaleriagonzalez/ABC-dataset
공식 코드Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
https://github.com/andreasgrv/unargmaxable
공식 코드Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice
https://github.com/andreeaiana/nase
공식 코드https://github.com/andreichertkov/tranfighterpro
공식 코드Translate your gibberish: black-box adversarial attack on machine translation systems
https://github.com/andrew-saydjari/EqWS.jl
공식 코드Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms
https://github.com/ankitdhall/lidar_camera_calibration
공식 코드https://github.com/anoidgit/transformer
공식 코드Neutron: An Implementation of the Transformer Translation Model and its Variants
https://github.com/anon294384/diffuseit
공식 코드Diffusion-based Image Translation using Disentangled Style and Content Representation
https://github.com/anonymous-ai-for-earth/satellite-to-satellite-translation
공식 코드https://github.com/anoopkunchukuttan/geomm
공식 코드Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
https://github.com/ansh941/MnistSimpleCNN
공식 코드An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition
https://github.com/antaegun20/CycleGANAS
공식 코드CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.