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

https://github.com/bhattg/Decay-RNN-ACL-SRW2020

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How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?

https://github.com/biaofuxmu/conslt

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A Token-level Contrastive Framework for Sign Language Translation

https://github.com/biaofuxmu/east

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LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline

https://github.com/bic4907/insturcted_pcgrl

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IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation

https://github.com/bidishasamantakgp/VACS

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A Deep Generative Model for Code-Switched Text

https://github.com/bigcat2333/derc

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A Simple yet Effective Self-Debiasing Framework for Transformer Models

https://github.com/bigdante/autore

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AutoRE: Document-Level Relation Extraction with Large Language Models

https://github.com/billy-inn/NFETC

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Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

https://github.com/billyzhang24kobe/ls_proficiency

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ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution

https://github.com/bin199/informin-cl

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An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

https://github.com/binwang28/evalrank-embedding-evaluation

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Just Rank: Rethinking Evaluation with Word and Sentence Similarities

https://github.com/binwang28/rse

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Relational Sentence Embedding for Flexible Semantic Matching

https://github.com/bionlplab/radtext

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Radiology Text Analysis System (RadText): Architecture and Evaluation

https://github.com/bionlproc/BERT-CRel-Embeddings

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Improved Biomedical Word Embeddings in the Transformer Era

https://github.com/bishanyang/EventEntityExtractor

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Joint Extraction of Events and Entities within a Document Context

https://github.com/biu-nlp/sprp-acl2018

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Split and Rephrase: Better Evaluation and a Stronger Baseline

https://github.com/blacknoodle/tucore-gcn

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Graph Based Network with Contextualized Representations of Turns in Dialogue

https://github.com/blender-nlp/newsclaims

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NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge

https://github.com/blitzprecision/ficle

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Neural models for Factual Inconsistency Classification with Explanations

https://github.com/blmoistawinde/fense

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Can Audio Captions Be Evaluated with Image Caption Metrics?

https://github.com/bloodraven66/ssl_aai

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Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models

https://github.com/bloomberg/cnn-rnf

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Convolutional Neural Networks with Recurrent Neural Filters

https://github.com/blpxspg/cone

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Cone: Unsupervised Contrastive Opinion Extraction

https://github.com/bnewm0609/refining-tse

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Refining Targeted Syntactic Evaluation of Language Models

https://github.com/bnmin/tdp_ranking

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Exploring Contextualized Neural Language Models for Temporal Dependency Parsing

https://github.com/bo1929/anelfop

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Focusing on Potential Named Entities During Active Label Acquisition

https://github.com/bohanhou14/semstamp

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SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

https://github.com/bohanli/BERT-flow

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On the Sentence Embeddings from Pre-trained Language Models

https://github.com/bojone/BERT-whitening

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Whitening Sentence Representations for Better Semantics and Faster Retrieval

https://github.com/boknilev/nmt-repr-analysis

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On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

https://github.com/boleima/topro

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ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

https://github.com/boo-18/gr-gan

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GR-GAN: Gradual Refinement Text-to-image Generation

https://github.com/borgr/GEC_UD_divergences

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Classifying Syntactic Errors in Learner Language

https://github.com/borgr/IBGEC

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Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification

https://github.com/borrisonxiao/icefall

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HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation

https://github.com/boschresearch/adversarial_meta_embeddings

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FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations

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