https://github.com/st-vincent1/cornell_rich
공식 코드Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
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/st-vincent1/mtcue
공식 코드https://github.com/stanford-oval/SPL
공식 코드Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
https://github.com/stanford-oval/dialogues
공식 코드Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
https://github.com/steinst/sentalign
공식 코드https://github.com/steveash/NETransliteration-COLING2018
공식 코드https://github.com/steventan0110/NMTModelAttack
공식 코드Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
https://github.com/styx97/PolarizationAAAI2021
공식 코드We Don't Speak the Same Language: Interpreting Polarization through Machine Translation
https://github.com/su-jiayuan/m-mad
공식 코드M-MAD: Multidimensional Multi-Agent Debate Framework for Fine-grained Machine Translation Evaluation
https://github.com/su0315/discourse_context_mt
공식 코드Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues
https://github.com/sufe-nlp/transformer-alignment
공식 코드Accurate Word Alignment Induction from Neural Machine Translation
https://github.com/sunbowliu/ptvsbt
공식 코드On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
https://github.com/sunzewei2715/Doc2Doc_NMT
공식 코드https://github.com/sunzewei2715/Graformer
공식 코드Multilingual Translation via Grafting Pre-trained Language Models
https://github.com/supertext/evaluation_deepl_supertext
공식 코드A comparison of translation performance between DeepL and Supertext
https://github.com/surafelml/Afro-NMT
공식 코드Low Resource Neural Machine Translation: A Benchmark for Five African Languages
https://github.com/surafelml/adapt-mnmt
공식 코드Adapting Multilingual Neural Machine Translation to Unseen Languages
https://github.com/suralk/multiner
공식 코드A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
https://github.com/surrey-nlp/LLM4MT_eval
공식 코드What do Large Language Models Need for Machine Translation Evaluation?
https://github.com/surrey-nlp/LLMs4MTQE-UGC
공식 코드https://github.com/surrey-nlp/PLOD-AbbreviationDetection
공식 코드PLOD: An Abbreviation Detection Dataset for Scientific Documents
https://github.com/surrey-nlp/homo_gen
공식 코드Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems
https://github.com/sutd-visual-computing-group/LS-KD-compatibility
공식 코드Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
https://github.com/suzyahyah/icl_anti-lm_decoding
공식 코드Anti-LM Decoding for Zero-shot In-context Machine Translation
https://github.com/swapniljadhav1921/marathi-2-english-NMT
공식 코드Marathi To English Neural Machine Translation With Near Perfect Corpus And Transformers
https://github.com/swaroop-nath/transformer-expressivity
공식 코드Transformers are Expressive, But Are They Expressive Enough for Regression?
https://github.com/swarupbehera/mAVQA
공식 코드https://github.com/sweta20/ComplexityControlledMT
공식 코드https://github.com/sydney-machine-learning/translationanalysis-mandarin
공식 코드https://github.com/syuqings/fashion-mmt
공식 코드Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training
https://github.com/szhangtju/The-compression-of-Transformer
공식 코드https://github.com/tagoyal/sow-reap-paraphrasing
공식 코드Neural Syntactic Preordering for Controlled Paraphrase Generation
https://github.com/takase/alone_seq2seq
공식 코드https://github.com/takase/rethink_perturbations
공식 코드Rethinking Perturbations in Encoder-Decoders for Fast Training
https://github.com/takase/share_layer_params
공식 코드https://github.com/tal-ai/RECLE
공식 코드Representation Learning from Limited Educational Data with Crowdsourced Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.