https://github.com/yeliu918/KG-BART
공식 코드KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Papers with Code 아카이브
Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/yeliu918/hetformer
공식 코드HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
https://github.com/yema2018/attention-aware
공식 코드Global-aware Beam Search for Neural Abstractive Summarization
https://github.com/yema2018/global_aware
공식 코드Global-aware Beam Search for Neural Abstractive Summarization
https://github.com/yezato11/INTERSPEECH_Media
공식 코드https://github.com/yeziyi1998/brain-language-generation
공식 코드https://github.com/yfzhang114/robustness-detection
공식 코드Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors
https://github.com/yg211/acl20-ref-free-eval
공식 코드SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
https://github.com/yichiac/crop-type-transfer-learning
공식 코드On the Generalizability of Foundation Models for Crop Type Mapping
https://github.com/yifan123/flow_grpo
공식 코드https://github.com/yilunzhao/llm-t2t
공식 코드https://github.com/yilunzhao/qtsumm
공식 코드https://github.com/ying-A/SandC
공식 코드https://github.com/yinghao-li/gno-ie
공식 코드https://github.com/yipingnus/scratchplot-story-generation
공식 코드https://github.com/yiqingxyq/doclens
공식 코드DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
https://github.com/yiren-jian/evlgen
공식 코드Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction
https://github.com/yiweiluo/immigrant-food-framing
공식 코드https://github.com/yixinL7/Direct-Style-Transfer
공식 코드https://github.com/yixinL7/SimCLS
공식 코드SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
https://github.com/yixinl7/pagesum
공식 코드https://github.com/yiyanxu/pigeon
공식 코드https://github.com/yizhen20133868/Retriever-Dialogue
공식 코드Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
https://github.com/ymoslem/adaptive-mt-llm
공식 코드https://github.com/ymoslem/mt-lm
공식 코드https://github.com/ymoslem/mt-preparation
공식 코드https://github.com/yoadtew/zero-shot-image-to-text
공식 코드ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
https://github.com/yoichi1484/reflection
공식 코드https://github.com/yonatanbitton/wysiwyr
공식 코드What You See is What You Read? Improving Text-Image Alignment Evaluation
https://github.com/yongcaoplus/cudialog
공식 코드Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys
https://github.com/yongchao98/codesteer-v1.0
공식 코드CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
https://github.com/yoonnajang/rem
공식 코드Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
https://github.com/yosukehiguchi/espnet
공식 코드https://github.com/young1993/tlm
공식 코드https://github.com/youseob/pin
공식 코드Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
https://github.com/ysmiura/ifcc
공식 코드Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.