https://github.com/krystalan/drt-o1
공식 코드DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
Papers with Code 아카이브
Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 84
- 보존된 평가 기록
- 438
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/kshitij98/vita
공식 코드https://github.com/kssteven418/biglittledecoder
공식 코드https://github.com/kt2k01/petci
공식 코드PETCI: A Parallel English Translation Dataset of Chinese Idioms
https://github.com/ku-nlp/video-helpful-mmt
공식 코드https://github.com/ku-nlp/visa
공식 코드VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
https://github.com/kurdishblark/kurdishnamedentities
공식 코드A Dataset of Kurdish (Sorani) Named Entities -- An Amendment to Kurdish-BLARK Named Entities
https://github.com/kyunghyuncho/WordVectorManifold
공식 코드Context-Dependent Word Representation for Neural Machine Translation
https://github.com/l3cube-pune/MarathiNLP
공식 코드https://github.com/laeubli/parity
공식 코드Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
https://github.com/lagom-nlp/transtokenizer
공식 코드https://github.com/lama-west/sparql_query_generation_aacl-ijcnl2022
공식 코드A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation
https://github.com/lancopku/AdaNorm
공식 코드https://github.com/lancopku/DeconvDec
공식 코드Deconvolution-Based Global Decoding for Neural Machine Translation
https://github.com/lancopku/Explicit-Sparse-Transformer
공식 코드Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
https://github.com/lancopku/MUSE
공식 코드MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
https://github.com/lancopku/bag-of-words
공식 코드https://github.com/lancopku/fedmnmt
공식 코드Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
https://github.com/lancopku/well-classified-examples-are-underestimated
공식 코드Well-classified Examples are Underestimated in Classification with Deep Neural Networks
https://github.com/lang-uk/dragoman
공식 코드Setting up the Data Printer with Improved English to Ukrainian Machine Translation
https://github.com/langtech-bsc/mt-evaluation
공식 코드MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation
https://github.com/laniqo-public/do-not-change-me
공식 코드https://github.com/laniqo-public/fame-mt
공식 코드FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes
https://github.com/larselund/sparsifying-bnns-with-lrt-and-nf
공식 코드Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
https://github.com/laurieburchell/exploring-diversity-bt
공식 코드Exploring Diversity in Back Translation for Low-Resource Machine Translation
https://github.com/lavine-lmu/bi-acl
공식 코드Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation
https://github.com/lavine-lmu/m4adapter
공식 코드$m^4Adapter$: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
https://github.com/lavine-lmu/rmlnmt
공식 코드Improving Both Domain Robustness and Domain Adaptability in Machine Translation
https://github.com/law-ai/milpac
공식 코드MILPaC: A Novel Benchmark for Evaluating Translation of Legal Text to Indian Languages
https://github.com/layer6ai-labs/dims
공식 코드DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation
https://github.com/lbh0830/TW-Hokkien-LLM
공식 코드Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems
https://github.com/learnitboy/perseus
공식 코드https://github.com/leduckhai/multimed-st
공식 코드MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
https://github.com/lemmonation/abnet
공식 코드Incorporating BERT into Parallel Sequence Decoding with Adapters
https://github.com/lemmonation/fcl-nat
공식 코드Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
https://github.com/lemon0830/codec
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.