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

https://github.com/vyraun/Half-Size

On Dimensional Linguistic Properties of the Word Embedding Space

https://github.com/wakafengfan/simcse-pytorch

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/wang-h/bert-relation-classification

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/wangxinyu0922/Second_Order_Parsing

Second-Order Semantic Dependency Parsing with End-to-End Neural Networks

https://github.com/wasiahmad/GATE

GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

https://github.com/wbglaeser/brexit-sentiment

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/webis-de/acl-24

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

https://github.com/webis-de/small-text

Active Discriminative Text Representation Learning

https://github.com/weiwang2330/sparse-structured-attention

A Regularized Framework for Sparse and Structured Neural Attention

https://github.com/weixsong/bilm-tf

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/weiyezhimeng/silent-guardian

Universal Sentence Encoder

https://github.com/weiylu/NLP

Sequence to Sequence Learning with Neural Networks

https://github.com/wenhuchen/data-to-text-evaluation-metric

CIDEr: Consensus-based Image Description Evaluation

https://github.com/wenlinyao/emnlp21-connectthedots

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories

https://github.com/whitneysattler/Skip-Thoughts

Skip-Thought Vectors

https://github.com/whw199833/gbiz_torch/blob/main/example/model/ContextNetModel_test.py

ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

https://github.com/willzli/bilstm_selfattention

A Structured Self-attentive Sentence Embedding

https://github.com/wilsonlau-uw/Extract-Follow-up-Recommendations-

Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset

https://github.com/windsuzu/Enhance-Chinese-to-Japanese-Neural-Machine-Translation

Neural Machine Translation: A Review and Survey

https://github.com/windsuzu/improving-NMT-ch-jp

Neural Machine Translation: A Review and Survey

https://github.com/wiseodd/controlled-text-generation

Convolutional Neural Networks for Sentence Classification

https://github.com/wjun0830/qd-detr

Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding

https://github.com/wl-research/nubia

NUBIA: NeUral Based Interchangeability Assessor for Text Generation

https://github.com/woctezuma/steam-descriptions

All-but-the-Top: Simple and Effective Postprocessing for Word Representations

https://github.com/woozzu/dong_iccv_2017

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

https://github.com/worksapplications/uzushio

Deduplicating Training Data Makes Language Models Better

https://github.com/worta/generate_apt_paraphrases

Towards Human Understanding of Paraphrase Types in ChatGPT

https://github.com/wouterkool/estimating-gradients-without-replacement

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

https://github.com/wutong8023/Lifelong_Relation_Detection_with_Meta_Learning

Sentence Embedding Alignment for Lifelong Relation Extraction

https://github.com/wy-ei/Text-CNN

Convolutional Neural Networks for Sentence Classification

https://github.com/x-lance/neusym-rag

Language Models are Few-Shot Learners

https://github.com/xalanq/chinese-sentiment-classification

A Structured Self-attentive Sentence Embedding

https://github.com/xashru/mixup-text

Augmenting Data with Mixup for Sentence Classification: An Empirical Study

https://github.com/xcfcode/Summarization-Papers

Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization

https://github.com/xhlulu/arxiv-assistant

Neural Machine Translation by Jointly Learning to Align and Translate

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