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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/deep-spin/chunk-based_knn-mt
공식 코드https://github.com/deep-spin/efficient_knn_mt
공식 코드https://github.com/deep-spin/entmax
공식 코드https://github.com/deep-spin/hallucinations-in-nmt
공식 코드https://github.com/deep-spin/lmt_hallucinations
공식 코드https://github.com/deep-spin/mcan-vqa-continuous-attention
공식 코드https://github.com/deep-spin/ot-hallucination-detection
공식 코드Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
https://github.com/deep-spin/qaware-decode
공식 코드https://github.com/deep-spin/quest-decoding
공식 코드QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
https://github.com/deep-spin/robust_mt_evaluation
공식 코드BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation
https://github.com/deep-spin/spec
공식 코드The Explanation Game: Towards Prediction Explainability through Sparse Communication
https://github.com/deep-spin/ssm-mt
공식 코드How Effective are State Space Models for Machine Translation?
https://github.com/deep-spin/translation-hypothesis-ensembling
공식 코드An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
https://github.com/deep-spin/treqa
공식 코드Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering
https://github.com/deep-spin/ua_comet
공식 코드https://github.com/deep-spin/uncertainties_mt_eval
공식 코드https://github.com/deeplearnxmu/bigvideo-vmt
공식 코드BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
https://github.com/deeplearnxmu/knn-mt-dr
공식 코드Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval
https://github.com/deeplearnxmu/mc_tit
공식 코드Exploring Better Text Image Translation with Multimodal Codebook
https://github.com/deeplearnxmu/revisedkey-knn-mt
공식 코드https://github.com/deeplearnxmu/robust-knn-mt
공식 코드https://github.com/deeplearnxmu/translatotron-v
공식 코드Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation
https://github.com/deepmipt/seq2seq_scg
공식 코드Using stochastic computation graphs formalism for optimization of sequence-to-sequence model
https://github.com/deeps73/CycleDistill
공식 코드CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
https://github.com/demelin/detecting_wsd_biases_for_nmt
공식 코드https://github.com/demelin/transformer_lexical_shortcuts
공식 코드Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts
https://github.com/dessertlab/Shellcode_IA32
공식 코드Shellcode_IA32: A Dataset for Automatic Shellcode Generation
https://github.com/devaansh100/cliptrans
공식 코드CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
https://github.com/devichand579/HPT
공식 코드https://github.com/dfki-nlp/multitacred
공식 코드MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
https://github.com/dgl-prc/rnn2automata
공식 코드Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
https://github.com/dialogue-evaluation/ruatd
공식 코드Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
https://github.com/dice-group/neuralclassexpressionsynthesis
공식 코드https://github.com/didzis/tensorflowAMR
공식 코드Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
https://github.com/dimitarsh1/nmt-ecofootprint
공식 코드The Ecological Footprint of Neural Machine Translation Systems
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