https://github.com/pytorch/fairseq/tree/master/examples/criss
공식 코드Cross-lingual Retrieval for Iterative Self-Supervised Training
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/pytorch/fairseq/tree/master/examples/fast_noisy_channel
공식 코드Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling
https://github.com/pytorch/fairseq/tree/master/examples/latent_depth
공식 코드https://github.com/pytorch/fairseq/tree/master/examples/mbart
공식 코드Multilingual Denoising Pre-training for Neural Machine Translation
https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text
공식 코드https://github.com/qcri/multilingual-latent-concepts
공식 코드https://github.com/quest-lab-iisc/Learn-Unlearn-Relearn-Noise-SLMs
공식 코드Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?
https://github.com/radidd/Doc-substructure-NMT
공식 코드https://github.com/raingo/TGIF-Release
공식 코드TGIF: A New Dataset and Benchmark on Animated GIF Description
https://github.com/ranqiu92/RecoverSAT
공식 코드Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation
https://github.com/raosudha89/GYAFC-corpus
공식 코드https://github.com/raphaelmerx/mambai
공식 코드https://github.com/raphaelmerx/tulun
공식 코드TULUN: Transparent and Adaptable Low-resource Machine Translation
https://github.com/rasoolims/ImageTranslate
공식 코드"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks
https://github.com/rasoolims/PBreak
공식 코드https://github.com/ratishsp/decomt
공식 코드Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
https://github.com/raymondhs/constrained-levt
공식 코드Lexically Constrained Neural Machine Translation with Levenshtein Transformer
https://github.com/rbawden/DiaBLa-dataset
공식 코드DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
https://github.com/rbawden/mt-bigscience
공식 코드Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM
https://github.com/rbawden/paraphrasing-bleu
공식 코드A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
https://github.com/recitalAI/QuestEval
공식 코드https://github.com/redpony/cdec
공식 코드Scalable Machine Translation in Memory Constrained Environments
https://github.com/research-anonymous/multiparallelfinetuning4mmt
공식 코드https://github.com/reubenharry/pragmatic-translation
공식 코드Lost in Machine Translation: A Method to Reduce Meaning Loss
https://github.com/rewicks/ctxpro
공식 코드Identifying Context-Dependent Translations for Evaluation Set Production
https://github.com/rgwt123/DialogueMT
공식 코드https://github.com/ricsinaruto/Seq2seqChatbots
공식 코드https://github.com/ringoreality/uniblock
공식 코드uniblock: Scoring and Filtering Corpus with Unicode Block Information
https://github.com/rizar/actor-critic-public
공식 코드https://github.com/rlitschk/modularclir
공식 코드Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
https://github.com/robertostling/hnmt
공식 코드https://github.com/roeeaharoni/unsupervised-domain-clusters
공식 코드https://github.com/romainpkq/chatgpt4mt
공식 코드https://github.com/romankoshkin/tollmatch
공식 코드https://github.com/rooshenas/ebr_mt
공식 코드Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models
https://github.com/roxot/mbr-nmt
공식 코드Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.