Papers with Code 아카이브

Sentence

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/andy194673/nlg-scvae

Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems

https://github.com/aneesha/SiameseBERT-Notebook

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/anhtu-phan/multi-domain-machine-translation

Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing

https://github.com/anhtunguyen98/NER-biaffine

Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers

https://github.com/aniketfadia96/Convolutional-Neural-Networks-for-Text-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/anirbrhm/ImageCaptioning

Show and Tell: A Neural Image Caption Generator

https://github.com/anirudharc/transformer

A Structured Self-attentive Sentence Embedding

https://github.com/anitakumarijena/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/anlausch/LIBERT

Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

https://github.com/anshulwadhawan/ABSA

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

https://github.com/ansonb/RECON

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

https://github.com/anthonysicilia/equitable-dialogue-acl2023

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

https://github.com/anthonysicilia/leather-aacl2022

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

https://github.com/anthonysicilia/pacbayes-adaptation-uai2022

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

https://github.com/antoine77340/howto100m

A Joint Sequence Fusion Model for Video Question Answering and Retrieval

https://github.com/antoyang/VidChapters

Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning

https://github.com/anuprulez/clade_prediction

Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

https://github.com/anutkk/RambaNet

Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

https://github.com/anzeyimana/kinyabert-acl2022

Rethinking Positional Encoding in Language Pre-training

https://github.com/aonotas/deep-crf

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/apcl-research/jam

Recommendations for Datasets for Source Code Summarization

https://github.com/appcoreopc/berty

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/applicaai/senteval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/appraisedev/ocelot

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

https://github.com/aqweteddy/BERTDistilForClassification

Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

https://github.com/aravindhv10/CPP_Wrappers

QCD-Aware Recursive Neural Networks for Jet Physics

https://github.com/ariepratama/syntax-driven-da

An Analysis of Simple Data Augmentation for Named Entity Recognition

https://github.com/arne-cl/feng-hirst-rst-parser

Two-pass Discourse Segmentation with Pairing and Global Features

https://github.com/arukavina/baking-lyrics

Skip-Thought Vectors

https://github.com/arvind385801/paraphrasegen

Generating Sentences from a Continuous Space

https://github.com/asad1996172/Authorship-attribution-using-CNN

Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

https://github.com/asadovsky/nn

Constituency Parsing with a Self-Attentive Encoder

https://github.com/asahi417/lmppl

Language Models are Few-Shot Learners

https://github.com/asahi417/relbert

pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

https://github.com/asappresearch/dialog-intent-induction

An efficient framework for learning sentence representations

https://github.com/aseidelo/wiki_generator

Generating Wikipedia by Summarizing Long Sequences

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