https://github.com/issam9/adapters-simt
공식 코드Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters
Papers with Code 아카이브
Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ivanwang0730/styleap
공식 코드Controlling Styles in Neural Machine Translation with Activation Prompt
https://github.com/ivn-rin/bio-med-bit
공식 코드Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models
https://github.com/ivulic/panlex-bli
공식 코드Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?
https://github.com/iyanush/nollysenti
공식 코드https://github.com/izecson/terminology-translation
공식 코드Rule-based Morphological Inflection Improves Neural Terminology Translation
https://github.com/j22melody/signwriting-translation
공식 코드Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting
https://github.com/jacobandreas/neuralese
공식 코드https://github.com/jacobkrantz/VertMetric
공식 코드https://github.com/jairsan/Stream-level_Latency_Evaluation_for_Simultaneous_Machine_Translation
공식 코드Stream-level Latency Evaluation for Simultaneous Machine Translation
https://github.com/jairsan/segmentation-free_streaming_machine_translation
공식 코드https://github.com/jamydon/scoi
공식 코드SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
https://github.com/jannik-brinkmann/multilingual-features
공식 코드https://github.com/jarfo/joint
공식 코드Joint Source-Target Self Attention with Locality Constraints
https://github.com/jbarnesspain/blse
공식 코드Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
https://github.com/jbarnesspain/fine-grained_cross-lingual_emotion
공식 코드https://github.com/jbarnesspain/targeted_blse
공식 코드Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study
https://github.com/jbdel/WMT18_MNMT
공식 코드https://github.com/jcyk/copyisallyouneed
공식 코드Neural Machine Translation with Monolingual Translation Memory
https://github.com/jcyk/gtos
공식 코드https://github.com/jdea-nlp/vega-mt
공식 코드Vega-MT: The JD Explore Academy Translation System for WMT22
https://github.com/jebivid/adversarial-nmt
공식 코드On Adversarial Examples for Character-Level Neural Machine Translation
https://github.com/jeffreyscheng/senior-thesis-translation
공식 코드https://github.com/jenlindadsouza/NLPContributions
공식 코드https://github.com/jerbarnes/fine-grained_cross-lingual_emotion
공식 코드https://github.com/jeticg/Code013-CL-LexPG
공식 코드Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation
https://github.com/jhu-clsp/kreyol-mt
공식 코드Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages
https://github.com/jiacheng-xu/lattice-generation
공식 코드https://github.com/jiangqn/kster
공식 코드Learning Kernel-Smoothed Machine Translation with Retrieved Examples
https://github.com/jiangyctarheel/sq-transformer
공식 코드Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
https://github.com/jiangyctarheel/undirected-generation
공식 코드Learning and Analyzing Generation Order for Undirected Sequence Models
https://github.com/jiaodali/vision-matters-when-it-should
공식 코드Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
https://github.com/jiarui-liu/multilingualaiterminology
공식 코드Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
https://github.com/jind11/DAMT
공식 코드A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
https://github.com/jingli9111/RUM
공식 코드https://github.com/jingli9111/RUM-Tensorflow
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.