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/timeseriesAI/tsai/tree/main/tsai/models
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/timrozday/spl-indications-bart
https://github.com/tjrwlgns1198/ChatBot
https://github.com/tjuhaoxiaotian/pymarl3
https://github.com/tmatha/lstm
https://github.com/tmtmaj/Korean-Chinese-parallel-dataset-for-machine-translation-task
Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue
https://github.com/tmtmaj/Korean-Chinese_dataset
Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue
https://github.com/tnq177/nmt_text_from_non_native_speaker
https://github.com/tomdyer10/fake_news
https://github.com/tomsercu/lstm
https://github.com/tonyzhaozh/few-shot-learning
https://github.com/transducens/LASERtrain
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/transm2/mmf3
https://github.com/transms/m2ts
https://github.com/tree-park/kor-to-eng-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/tree-park/transformer_lm
https://github.com/trevor-richardson/rnn_zoo
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/trqminh/seq2seq
https://github.com/truclycs/ocr_seq2seq
https://github.com/tshi04/LeafNATS
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
https://github.com/tshu-w/dbcopilot
https://github.com/tshu-w/llm4em
https://github.com/tsujuifu/pytorch_lstm-shuttle
https://github.com/turkunlp/megatron-deepspeed
https://github.com/tvinith/bert
https://github.com/tw-yuhsi/a-new-perspective-for-shuttlecock-hitting-event-detection
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/txsun1997/metric-fairness
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/tyxr/bert
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/uakarsh/Eit-Enhanced-Interactive-Transformer
https://github.com/ubc-nlp/coling2022
Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings
https://github.com/ucam-smt/sgnmt
SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
https://github.com/uclanlp/visualbert
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
https://github.com/udnet96/BART-various-finetune
https://github.com/uds-lsv/menyo-20k_mt
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.