Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/open-mmlab/mmgeneration
https://github.com/openai/gpt-2
https://github.com/openai/minif2f
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/opennlg/openba-v2
https://github.com/opennmt/ctranslate2
https://github.com/osu-nlp-group/llm-cn-eval
https://github.com/oswaldoludwig/Parallel-Seq2Seq
End-to-end Adversarial Learning for Generative Conversational Agents
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/owainwest/uniprot_bert
https://github.com/paolanu/BERT_epitope
https://github.com/parajain/data-to-text
Neural Text Generation from Structured Data with Application to the Biography Domain
https://github.com/parajain/structscribe
Scalable Micro-planned Generation of Discourse from Structured Data
https://github.com/parcorelab/comscribe
https://github.com/parkervg/blendsql
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
https://github.com/passing2961/empgpt-3
What makes a good conversation? How controllable attributes affect human judgments
https://github.com/passing2961/personachatgen
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/patil-suraj/question_generation
Simplifying Paragraph-level Question Generation via Transformer Language Models
https://github.com/patrickhart/jaxdl
https://github.com/patrickpynadath1/automatic_cyclical_sampling
Controlled LLM Decoding via Discrete Auto-regressive Biasing
https://github.com/paulskeie/stadnamn
https://github.com/pbcquoc/transformer
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/pcyin/NL2code
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/pcyin/pytorch_basic_nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/pedrobragap/products_classification
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/pengbaolin/soloist
Few-shot Natural Language Generation for Task-Oriented Dialog
https://github.com/pengming617/bert_classification
https://github.com/pengshuyuan/Bert
https://github.com/penny9287/sockeye
https://github.com/pennylaneai/generative-quantum-states
https://github.com/phaelishall/semeval2020-code
HuggingFace's Transformers: State-of-the-art Natural Language Processing
https://github.com/phanideepgampa/IM2LATEX
https://github.com/phbst/tinyrag
https://github.com/philipperemy/keras-attention-mechanism
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/phohenecker/pytorch-transformer
https://github.com/pierrecolombo/nlg_eval_via_simi_measures
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.