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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/timbmg/Sentence-VAE

Generating Sentences from a Continuous Space

https://github.com/timerstime/SDG4DA

Convolutional Neural Networks for Sentence Classification

https://github.com/timonziegenbein/inappropriateness-mitigation

LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback

https://github.com/tnq177/improving_lexical_choice_in_nmt

Incorporating Discrete Translation Lexicons into Neural Machine Translation

https://github.com/tomekkorbak/pretraining-with-human-feedback

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

https://github.com/tomwalczak/PubMed-Abstract-Analyzer

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

https://github.com/tonyzhaozh/few-shot-learning

Language Models are Few-Shot Learners

https://github.com/toriving/KoEDA

AEDA: An Easier Data Augmentation Technique for Text Classification

https://github.com/toru34/kim_emnlp_2014

Convolutional Neural Networks for Sentence Classification

https://github.com/toru34/rush_emnlp_2015

A Neural Attention Model for Abstractive Sentence Summarization

https://github.com/toru34/zhou_acl_2017

Selective Encoding for Abstractive Sentence Summarization

https://github.com/toumix/discopy

Mathematical Foundations for a Compositional Distributional Model of Meaning

https://github.com/transducens/LASERtrain

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/transms/m2ts

Recommendations for Datasets for Source Code Summarization

https://github.com/tree-park/kor-to-eng-translation

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/trisongz/question-answering-lightning

Learning to Ask: Neural Question Generation for Reading Comprehension

https://github.com/triton-inference-server/fastertransformer_backend

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/trqminh/seq2seq

Sequence to Sequence Learning with Neural Networks

https://github.com/ttengwang/coco-caption

CIDEr: Consensus-based Image Description Evaluation

https://github.com/tuetschek/e2e-metrics

CIDEr: Consensus-based Image Description Evaluation

https://github.com/turkunlp/megatron-deepspeed

Language Models are Few-Shot Learners

https://github.com/tuzhaopeng/nmt

Neural Machine Translation with Reconstruction

https://github.com/twairball/keras_lstm_vae

Generating Sentences from a Continuous Space

https://github.com/twisha96/Natural-Language-Processing

Not All Attention Is Needed: Gated Attention Network for Sequence Data

https://github.com/tylin/coco-caption

CIDEr: Consensus-based Image Description Evaluation

https://github.com/tyliupku/HaDeS

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

https://github.com/uabinf/nlp-fall-2019-project-anuradha_shinjitha

Text Summarization with Pretrained Encoders

https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group8

Political Speech Generation

https://github.com/uhh-lt/amharicmodels

The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

https://github.com/um-arm-lab/efficient-eng-2-ltl

Language Models are Few-Shot Learners

https://github.com/umeiko/mindspore-seq2seq

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/umutguneri/Question-Answering-Assistant

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/unbabel/mt-telescope

To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation

https://github.com/unik00/textCNN-pytorch

Convolutional Neural Networks for Sentence Classification

https://github.com/unik00/textCNN-tf2

Convolutional Neural Networks for Sentence Classification

https://github.com/universaldependencies/ud_frisian_dutch-fame

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.