https://github.com/uabinf/nlp-fall-2019-project-anuradha_shinjitha
Unified Language Model Pre-training for Natural Language Understanding and Generation
Papers with Code 아카이브
Text Generation
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- 평가표
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- 보존된 평가 기록
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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/uci-soe/fairytaleqadata
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group8
Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts
https://github.com/udnet96/BART-various-finetune
https://github.com/ufal/wembedding_service
HuggingFace's Transformers: State-of-the-art Natural Language Processing
https://github.com/umass-embodied-agi/coela
Building Cooperative Embodied Agents Modularly with Large Language Models
https://github.com/umass-foundation-model/co-llm-agents
Building Cooperative Embodied Agents Modularly with Large Language Models
https://github.com/umeiko/mindspore-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/umutguneri/Question-Answering-Assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/universebh/text_generation_fsa_gan
Long Text Generation via Adversarial Training with Leaked Information
https://github.com/urchade/molgen
https://github.com/utanaka2000/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/utsjiyaoli/qa-attack
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/uzi0espil/research-papers-implementation/tree/master/Effective%20Approaches%20to%20Attention-based%20Neural%20Machine%20Translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/uzi0espil/research-papers-implementation/tree/master/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vGkatsis/Chat_Bot_DL
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/valentinmace/noisy-text
https://github.com/valko073/LyricsGANs
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varshakr24/CommonsenseReasoning
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
https://github.com/varun-bhaseen/Image-caption-generation-using-attention-model
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/varun-suresh/experiments-with-gpt2
https://github.com/vectara/hallucination-leaderboard
https://github.com/vedantc6/SeqGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/veekaybee/what_are_embeddings
https://github.com/vgaraujov/seq2seq-spanish-plms
https://github.com/vijay120/bert
https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/vijini/FM-GAN
https://github.com/vikashranjan/transformer
https://github.com/vikua/keras-attention-models
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vinairesearch/phogpt
https://github.com/vinayak19th/Brevis-2.0
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.