https://github.com/pengr/ikd-mmt
공식 코드Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
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/persiannlp/parsinlu
공식 코드ParsiNLU: A Suite of Language Understanding Challenges for Persian
https://github.com/phmeier/smaragd
공식 코드SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance
https://github.com/phuongnm94/PhraseTransformer
공식 코드An Effective Method using Phrase Mechanism in Neural Machine Translation
https://github.com/pkunlp-icler/mls
공식 코드Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation
https://github.com/pkunlp-icler/off-target-mnmt
공식 코드On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
https://github.com/pkuyym/evolvingattention
공식 코드https://github.com/plippmann/multimodal-manga-translation
공식 코드Context-Informed Machine Translation of Manga using Multimodal Large Language Models
https://github.com/plkumjorn/GrASP
공식 코드GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
https://github.com/pluslabnlp/clap
공식 코드Contextual Label Projection for Cross-Lingual Structured Prediction
https://github.com/pluslabnlp/dinado
공식 코드DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
https://github.com/pmichel31415/P-DRO
공식 코드Learning Neural Models for Natural Language Processing in the Face of Distributional Shift
https://github.com/pmichel31415/mtnt
공식 코드https://github.com/pmichel31415/teapot-nlp
공식 코드On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
https://github.com/poethan/AlphaMWE
공식 코드AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations
https://github.com/poethan/MWE4MT
공식 코드MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
https://github.com/posenhuang/NPMT
공식 코드https://github.com/potamides/unsupervised-metrics
공식 코드USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation
https://github.com/pppa2019/ambgimt
공식 코드Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words
https://github.com/pppa2019/swie_overmiss_llm4mt
공식 코드Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
https://github.com/pprett/bolt
공식 코드Cross-Lingual Adaptation using Structural Correspondence Learning
https://github.com/pranjalchitale/in-context-mt-analysis
공식 코드An Empirical Study of In-context Learning in LLMs for Machine Translation
https://github.com/prashantksharma/CaCD
공식 코드https://github.com/prateeksaxena2809/EPIE_Corpus
공식 코드https://github.com/preceptorai-org/nllb_cs_em_nlp2024
공식 코드On Creating an English-Thai Code-switched Machine Translation in Medical Domain
https://github.com/primeqa/primeqa
공식 코드https://github.com/princeton-nlp/trime
공식 코드https://github.com/projecte-aina/plume
공식 코드Investigating the translation capabilities of Large Language Models trained on parallel data only
https://github.com/prometheus-eval/prometheus-eval
공식 코드https://github.com/protonish/cipherdaug-nmt
공식 코드CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation
https://github.com/pruthwik/odia-chunker
공식 코드https://github.com/pschwllr/MolecularTransformer
공식 코드Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
https://github.com/pygongnlp/pt-m2
공식 코드Revisiting Grammatical Error Correction Evaluation and Beyond
https://github.com/pytorch/fairseq
공식 코드Classical Structured Prediction Losses for Sequence to Sequence Learning
https://github.com/pytorch/fairseq/tree/main/examples/xglm
공식 코드https://github.com/pytorch/fairseq/tree/master/examples/byte_level_bpe
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.