https://github.com/apple/ml-kge
공식 코드Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
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/apple/ml-sigma-reparam
공식 코드Stabilizing Transformer Training by Preventing Attention Entropy Collapse
https://github.com/apple/ml-translate-vis
공식 코드Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
https://github.com/arabelatso/semmt
공식 코드SemMT: A Semantic-based Testing Approach for Machine Translation Systems
https://github.com/arbaazQureshi/attention_based_multimodal_fusion_for_estimating_depression
공식 코드Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations
https://github.com/arbml/taqyim
공식 코드https://github.com/arianna-bis/freeorder-mt
공식 코드On the Difficulty of Translating Free-Order Case-Marking Languages
https://github.com/aries-lm/lexmatcher-mt
공식 코드LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
https://github.com/armelrandy/icl-mt
공식 코드In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation
https://github.com/artetxem/esxnli
공식 코드https://github.com/artetxem/monoses
공식 코드https://github.com/artetxem/undreamt
공식 코드https://github.com/artetxem/vecmap
공식 코드https://github.com/arthurxlw/cytonMt
공식 코드CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++
https://github.com/artursstaf/mitigating-gender-bias-wmt-2020
공식 코드Mitigating Gender Bias in Machine Translation with Target Gender Annotations
https://github.com/asappresearch/imitkd
공식 코드Autoregressive Knowledge Distillation through Imitation Learning
https://github.com/asappresearch/sru
공식 코드https://github.com/ashwinkalyan/dbs
공식 코드Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/asif6827/gri
공식 코드GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
https://github.com/asking28/offenseval2020
공식 코드https://github.com/asking28/sentimix2020
공식 코드https://github.com/astonzhang/Parameterization-of-Hypercomplex-Multiplications
공식 코드https://github.com/astridesa/edueval
공식 코드Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
https://github.com/asyml/texar
공식 코드Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
https://github.com/atnafuatx/ethionmt-datasets
공식 코드The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
https://github.com/attilanagy234/syntax-augmentation-nmt
공식 코드Syntax-based data augmentation for Hungarian-English machine translation
https://github.com/attilanagy234/treeswap
공식 코드TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping
https://github.com/automl/modnas
공식 코드https://github.com/auttention/SleepyAttention
공식 코드A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech
https://github.com/av9ash/English-Please
공식 코드https://github.com/avmb/mt_scaling_prompt_injection
공식 코드Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks
https://github.com/awslabs/multilingual-top
공식 코드Multilingual Neural Semantic Parsing for Low-Resourced Languages
https://github.com/awslabs/sockeye
공식 코드https://github.com/ayushbits/saamayik
공식 코드Sāmayik: A Benchmark and Dataset for English-Sanskrit Translation
https://github.com/babangain/indicmmt
공식 코드Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
https://github.com/babangain/unsup_questions_translation
공식 코드Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.