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://bitbucket.org/antonis/nmt-grammar-noise
공식 코드https://bitbucket.org/gg42554/cl-sts
공식 코드A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
https://bitbucket.org/nlpitu/xnli
공식 코드https://bitbucket.org/robvanderg/xsid
공식 코드https://bitbucket.org/rumschuettel/rvqe
공식 코드https://bitbucket.org/tsendeemts/nse
공식 코드https://github.com/0nutation/dub
공식 코드https://github.com/180041123-atiq/mteonlowresourcelanguage
공식 코드https://github.com/2ral/Modelizer
공식 코드Learning Program Behavioral Models from Synthesized Input-Output Pairs
https://github.com/3dmaisons/af-mnt
공식 코드https://github.com/4mekki4/promap
공식 코드ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
https://github.com/573phn/rnn_typology
공식 코드On the Difficulty of Translating Free-Order Case-Marking Languages
https://github.com/7201krap/csce679_data-visualization
공식 코드https://github.com/AI4Bharat/IndicXlit
공식 코드Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users
https://github.com/AI4Bharat/indic-bart
공식 코드IndicBART: A Pre-trained Model for Indic Natural Language Generation
https://github.com/AI4LIFE-GROUP/LLM_Explainer
공식 코드In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
https://github.com/AIPHES/ACL20-Reference-Free-MT-Evaluation
공식 코드https://github.com/Adaxry/conf_aware_ss4nmt
공식 코드Confidence-Aware Scheduled Sampling for Neural Machine Translation
https://github.com/Aditi138/Embeddings
공식 코드https://github.com/AkariAsai/XORQA
공식 코드https://github.com/AkariAsai/extractive_rc_by_runtime_mt
공식 코드Multilingual Extractive Reading Comprehension by Runtime Machine Translation
https://github.com/AlexJonesNLP/SentimentMT
공식 코드https://github.com/AlexMoreo/word-class-embeddings
공식 코드https://github.com/AliOsm/shakkelha
공식 코드https://github.com/Alidar40/lez-rus-azj-translator
공식 코드Neural machine translation system for Lezgian, Russian and Azerbaijani languages
https://github.com/Avmb/adversarial_MT_prompt_injection
공식 코드A test suite of prompt injection attacks for LLM-based machine translation
https://github.com/Avmb/code-docstring-corpus
공식 코드https://github.com/Avmb/deep-nmt-architectures
공식 코드https://github.com/AzureStarz/paying_attention_to_the_source
공식 코드https://github.com/BITHLP/DRDA
공식 코드Deterministic Reversible Data Augmentation for Neural Machine Translation
https://github.com/BartoszPiotrowski/guiding-connection-tableau-by-RNNs
공식 코드Guiding Inferences in Connection Tableau by Recurrent Neural Networks
https://github.com/BestActionNow/SemiSupBLI
공식 코드Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction
https://github.com/Bhashini-IITJ/visualTranslation
공식 코드https://github.com/C4AI/Pira
공식 코드Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean
https://github.com/CAHLR/skill-equivalency
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.