https://github.com/ictnlp/streamspeech
공식 코드StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning
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/ictnlp/su4mt
공식 코드https://github.com/ictnlp/wait-info
공식 코드https://github.com/ictnlp/zero-mnmt
공식 코드Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings
https://github.com/idiap/Attentive_Residual_Connections_NMT
공식 코드Self-Attentive Residual Decoder for Neural Machine Translation
https://github.com/idiap/HAN_NMT
공식 코드Document-Level Neural Machine Translation with Hierarchical Attention Networks
https://github.com/idiap/drill
공식 코드https://github.com/idiap/joint-embedding-nmt
공식 코드Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation
https://github.com/idiap/sense_aware_NMT
공식 코드Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
https://github.com/idiap/translation-aided-slu
공식 코드https://github.com/ijauregiCMCRC/Shared_Attention_for_APE
공식 코드A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems
https://github.com/ijdutse/hausa-corpus
공식 코드The first large scale collection of diverse Hausa language datasets
https://github.com/ikergarcia1996/Easy-Translate
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/annotation-projection-app
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/easy-label-projection
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/t-projection
공식 코드T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
https://github.com/imperialite/uniform-complexity-textgen
공식 코드https://github.com/indian-nlp/assamese-dataset
공식 코드Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset Repository
https://github.com/indonlp/nusax
공식 코드NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
https://github.com/inseq-team/inseq
공식 코드Inseq: An Interpretability Toolkit for Sequence Generation Models
https://github.com/instance-wise-ordered-transformer/IOT
공식 코드IOT: Instance-wise Layer Reordering for Transformer Structures
https://github.com/intel/neural-compressor
공식 코드https://github.com/intellabs/fp8-emulation-toolkit
공식 코드https://github.com/irshadbhat/nsdp-cs
공식 코드Universal Dependency Parsing for Hindi-English Code-switching
https://github.com/irugina/AP
공식 코드Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
https://github.com/irugina1/llama-attention-pruning
공식 코드Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
https://github.com/is2ai/kazparc
공식 코드https://github.com/is2ai/kazqad
공식 코드https://github.com/is2ai/turkictts
공식 코드Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration
https://github.com/ischlag/fast-weight-transformers
공식 코드https://github.com/isenglab/stp
공식 코드https://github.com/ishan00/translation-for-code-switching-acl
공식 코드From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text
https://github.com/isi-nlp/Zoph_RNN
공식 코드https://github.com/isi-nlp/nlcodec
공식 코드Many-to-English Machine Translation Tools, Data, and Pretrained Models
https://github.com/isi-nlp/stamp
공식 코드https://github.com/ispras/atr4s
공식 코드ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.