https://github.com/hsgodhia/hred
Mutual Information and Diverse Decoding Improve Neural Machine Translation
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/ht1221/leap-lstm
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
https://github.com/huangxt39/BART_on_COVID_dialogue
https://github.com/hubiodatalab/druggen
https://github.com/huggingface/pytorch-image-models
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/huggingface/transformers
https://github.com/hugozhl/hetu
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
https://github.com/huulinhcvp/chatBot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/huyz2023/2by4-pretrain
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
https://github.com/hwzwx1190522/gru
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/hyang0129/foodclassapp
Regularizing Neural Networks by Penalizing Confident Output Distributions
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/asian-bart
Multilingual Denoising Pre-training for Neural Machine Translation
https://github.com/hyunwoongko/transformer
https://github.com/i-machine-think/machine-tasks
https://github.com/i2r-simmc/i2r-simmc-2020
https://github.com/i404788/DPFP-pytorch
https://github.com/iamjanvijay/rnnt_decoder_cuda
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/icewing1996/bert_dep
https://github.com/ictmcg/endef-sigir2022
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
https://github.com/ictnlp/LSG
Agent-SiMT: Agent-assisted Simultaneous Machine Translation with Large Language Models
https://github.com/ictnlp/nast-s2x
Glancing Transformer for Non-Autoregressive Neural Machine Translation
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/idsia/modern-srwm
https://github.com/idsia/neuraldiffeq-fwp
https://github.com/iejMac/ScriptWriter
https://github.com/iinemo/lm-polygraph
Uncertainty Estimation in Autoregressive Structured Prediction
https://github.com/ijauregiCMCRC/ReWE_NMT
Stronger Baselines for Trustable Results in Neural Machine Translation
https://github.com/ikergarcia1996/Iker-Garcia-Ferrero
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/imamathcat/LASER_Dependencies
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/imatge-upc/vqa-2016-cvprw
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
https://github.com/imperialnlp/vtlm
Cross-lingual Visual Pre-training for Multimodal Machine Translation
https://github.com/indobenchmark/indobenchmark-toolkit
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
https://github.com/indrajeet95/Device-Placement-Optimization-with-Reinforcement-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.