https://github.com/shirawein/amr-translationese
공식 코드Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation
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/shiyunee/facet-generation
공식 코드A Comparative Study of Training Objectives for Clarification Facet Generation
https://github.com/shizhediao/davinci
공식 코드Write and Paint: Generative Vision-Language Models are Unified Modal Learners
https://github.com/shlurbee/dmrs-text-generation-naacl2019
공식 코드https://github.com/shmsw25/factscore
공식 코드FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/shobrook/communities
공식 코드SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
https://github.com/showlab/FAR
공식 코드Long-Context Autoregressive Video Modeling with Next-Frame Prediction
https://github.com/shreyansh26/SRST-18
공식 코드IIT (BHU) Varanasi at MSR-SRST 2018: A Language Model Based Approach for Natural Language Generation
https://github.com/shrimai/Focused-Attention-Improves-Document-Grounded-Generation
공식 코드https://github.com/shrimai/Style-Transfer-Through-Back-Translation
공식 코드https://github.com/shs910/psst
공식 코드PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
https://github.com/shs910/tsst
공식 코드PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
https://github.com/shtuplus/iccc_cvpr2024
공식 코드Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning
https://github.com/shtuplus/pix2grp_cvpr2024
공식 코드From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models
https://github.com/shubhamagarwal92/mmd
공식 코드Improving Context Modelling in Multimodal Dialogue Generation
https://github.com/shuhaibm/heap
공식 코드Automatic Evaluation of Generative Models with Instruction Tuning
https://github.com/shuhewang1998/gpt-ner
공식 코드https://github.com/shumingma/SRB
공식 코드A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
https://github.com/shumpei19/jet
공식 코드Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation
https://github.com/shuoli90/rank-calibration
공식 코드Uncertainty in Language Models: Assessment through Rank-Calibration
https://github.com/shuzhouyuan/eval_g2t_genmodels
공식 코드https://github.com/siat-nlp/TransDG
공식 코드Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
https://github.com/siddk/voltron-evaluation
공식 코드https://github.com/siddk/voltron-robotics
공식 코드https://github.com/siemens/industrialbenchmark
공식 코드https://github.com/silverriver/mmchat
공식 코드https://github.com/silverriver/ood4nlu
공식 코드Out-of-domain Detection for Natural Language Understanding in Dialog Systems
https://github.com/silversolver/ai_boundary_detection
공식 코드https://github.com/simon-p-j-r/medref
공식 코드Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
https://github.com/simplenlg/simplenlg
공식 코드https://github.com/simtony/bart-word-orderer
공식 코드On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
https://github.com/sinhala-nlp/nsina
공식 코드https://github.com/sinhala-nlp/sinhala-headline-generation
공식 코드https://github.com/sinhala-nlp/sinhala-news-category-prediction
공식 코드https://github.com/sinhala-nlp/sinhala-news-media-identification
공식 코드https://github.com/sixiaozheng/contextualstory
공식 코드ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.