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

https://github.com/rgalhama/diora

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders

https://github.com/rhythmcao/text2sql-lgesql

Dual Learning for Semi-Supervised Natural Language Understanding

https://github.com/richa912/THANOS

A Fast Unified Model for Parsing and Sentence Understanding

https://github.com/richinkabra/CoVe-BCN

Convolutional Neural Networks for Sentence Classification

https://github.com/ridakadri14/AspectBasedSentimentAnalysis

Effective LSTMs for Target-Dependent Sentiment Classification

https://github.com/rileynwong/pytorch-seq2seq-joke2punchline

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/ringos/nfc-parser

Constituency Parsing with a Self-Attentive Encoder

https://github.com/rishavbb/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/riversdie/chatbot

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/rmslick/SummarySearch

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/rockandroll123/natural-language-inference

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/roeeaharoni/sprp-acl2018

Split and Rephrase: Better Evaluation and a Stronger Baseline

https://github.com/romankoshkin/transllama

TransLLaMa: LLM-based Simultaneous Translation System

https://github.com/roomylee/self-attentive-emb-tf

A Structured Self-attentive Sentence Embedding

https://github.com/royira/linguistic-binding-in-diffusion-models

Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment

https://github.com/rpryzant/delete_retrieve_generate

Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

https://github.com/ruc-aimc-lab/nt2vr

A Joint Sequence Fusion Model for Video Question Answering and Retrieval

https://github.com/ruc-nlpir/flashrag

Active Retrieval Augmented Generation

https://github.com/rudongyu/logire

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

https://github.com/rungjoo/Stable-Style-Transformer

Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer

https://github.com/runqitian/CR-CNN

Classifying Relations by Ranking with Convolutional Neural Networks

https://github.com/runzhouge/MAC

TALL: Temporal Activity Localization via Language Query

https://github.com/rupes438/CodeGen

Long Text Generation via Adversarial Training with Leaked Information

https://github.com/rvoleti89/icaasp-2019-word-subs

Investigating the Effects of Word Substitution Errors on Sentence Embeddings

https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tnt.py

Transformer in Transformer

https://github.com/ryankiros/skip-thoughts

Skip-Thought Vectors

https://github.com/ryankiros/visual-semantic-embedding

Multimodal Convolutional Neural Networks for Matching Image and Sentence

https://github.com/ryokamoi/cnn_for_sentence_classification

Convolutional Neural Networks for Sentence Classification

https://github.com/ryokamoi/original_textvae

Generating Sentences from a Continuous Space

https://github.com/s-ankur/fairseq-gec

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/s-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/s1998/All-but-the-top

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

https://github.com/sabetAI/bucc-eval

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/sachin19/mucoco

Controlled Text Generation as Continuous Optimization with Multiple Constraints

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