https://github.com/gjwubyron/evo
공식 코드Evaluating Automatic Metrics with Incremental Machine Translation Systems
Papers with Code 아카이브
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/google-research-datasets/ToTTo
공식 코드https://github.com/google-research-datasets/dakshina
공식 코드Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
https://github.com/google-research-datasets/indic-gen-bench
공식 코드https://github.com/google-research-datasets/mittens
공식 코드https://github.com/google-research/bleurt
공식 코드https://github.com/google-research/crosslingual-knowledge-barriers
공식 코드Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
https://github.com/google-research/flan
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/felix
공식 코드https://github.com/google-research/google-research/tree/master/perso_arabic_norm
공식 코드https://github.com/google-research/google-research/tree/master/scalable_shampoo/jax
공식 코드https://github.com/google-research/google-research/tree/master/sm3
공식 코드https://github.com/google-research/language/tree/master/language/labs/consistent_zero_shot_nmt
공식 코드Consistency by Agreement in Zero-shot Neural Machine Translation
https://github.com/google-research/mt-metrics-eval
공식 코드Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
https://github.com/google-research/nisaba
공식 코드Beyond Arabic: Software for Perso-Arabic Script Manipulation
https://github.com/google-research/prompt-tuning
공식 코드Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
https://github.com/google-research/url-nlp
공식 코드Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation
https://github.com/google-research/xtreme-up
공식 코드XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
https://github.com/google/aqt
공식 코드https://github.com/google/sentencepiece
공식 코드Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
https://github.com/google/seq2seq
공식 코드Massive Exploration of Neural Machine Translation Architectures
https://github.com/google/wmt-mqm-human-evaluation
공식 코드Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
https://github.com/google/wmt19-paraphrased-references
공식 코드https://github.com/googlei18n/transliteration
공식 코드Sequence-to-sequence neural network models for transliteration
https://github.com/gorokoba560/norm-analysis-of-transformer
공식 코드Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
https://github.com/gospelnnadi/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/gpengzhi/Bi-SimCut
공식 코드Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
https://github.com/gpengzhi/CrossConST-MT
공식 코드https://github.com/gpengzhi/crossconst-llm
공식 코드Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
https://github.com/gpengzhi/crossconst-sr
공식 코드Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization
https://github.com/gpengzhi/simcr
공식 코드An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation
https://github.com/graal-research/risc
공식 코드RISC: Generating Realistic Synthetic Bilingual Insurance Contract
https://github.com/grammatical/neural-naacl2018
공식 코드Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task
https://github.com/graykode/commit-autosuggestions
공식 코드CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model
https://github.com/gregor-ge/babel-imagenet
공식 코드Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.