https://github.com/cambridgeltl/adversarial-postspec
공식 코드Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
Papers with Code 아카이브
Lexical Simplification
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The goal of Lexical Simplification is to replace complex words (typically words that are used less often in language and are therefore less familiar to readers) with their simpler synonyms, without infringing the grammaticality and changing the meaning of the text.
Source: Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dennlinger/tsar-2022-shared-task
공식 코드UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?
https://github.com/kimchengsheang/conls
공식 코드https://github.com/kimchengsheang/mtls
공식 코드Multilingual Controllable Transformer-Based Lexical Simplification
https://github.com/kpkqwq/lspg
공식 코드Multilingual Lexical Simplification via Paraphrase Generation
https://github.com/lastus-taln-upf/tsar-2022-shared-task
공식 코드Lexical Simplification Benchmarks for English, Portuguese, and Spanish
https://github.com/mounicam/lexical_simplification
공식 코드A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
https://github.com/pytorch/fairseq
공식 코드https://github.com/qiang2100/BERT-LS
공식 코드https://github.com/razvanalex-phd/cwi_llm
공식 코드https://github.com/rlsnlp/simplebart
공식 코드Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification
https://github.com/twadada/lexsub_decontextualised
공식 코드Unsupervised Lexical Simplification with Context Augmentation
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/CalumPerrio/WNUT-2020
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/IndicoDataSolutions/finetune
https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering
https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py
https://github.com/SindhuMadi/FakeNewsDetection
https://github.com/Tencent/TurboTransformers
https://github.com/UnknownGenie/altered-BERT-KPE
https://github.com/abdumaa/hiqualprop
https://github.com/abhishekanand1710/noiseandbias
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/anlausch/LIBERT
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
https://github.com/awslabs/mlm-scoring
https://github.com/bcaitech1/p2-klue-Heeseok-Jeong
https://github.com/benywon/ReCO
https://github.com/bfopengradient/NLP_ROBERTA
https://github.com/blawok/named-entity-recognition
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.