https://github.com/rktamplayo/Entity2Topic
공식 코드Entity Commonsense Representation for Neural Abstractive Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Entity Commonsense Representation for Neural Abstractive Summarization
Translations as Additional Contexts for Sentence Classification
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Document-Level Text Simplification: Dataset, Criteria and Baseline
Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification
On the Trade-off between Redundancy and Local Coherence in Summarization
Process-Level Representation of Scientific Protocols with Interactive Annotation
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
What Was Your Prompt? A Remote Keylogging Attack on AI Assistants
EASSE-DE: Easier Automatic Sentence Simplification Evaluation for German
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
SENT: Sentence-level Distant Relation Extraction via Negative Training
Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected Situations
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
Debiased Contrastive Learning of Unsupervised Sentence Representations
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Learning to Imagine: Visually-Augmented Natural Language Generation
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Transforming Multi-Conditioned Generation from Meaning Representation
CIDEr-R: Robust Consensus-based Image Description Evaluation
Contextual Clarity: Generating Sentences with Transformer Models using Context-Reverso Data
Local and Global Context-Based Pairwise Models for Sentence Ordering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.