https://github.com/shreydesai/cups
공식 코드Compressive Summarization with Plausibility and Salience Modeling
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shrimai/Style-Transfer-Through-Back-Translation
공식 코드https://github.com/shrimai/Topological-Sort-for-Sentence-Ordering
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/shtoshni92/speech_parsing
공식 코드Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
https://github.com/shubhamkumarnigam/coliee-22
공식 코드https://github.com/shuhewang1998/sim-gpt
공식 코드https://github.com/shuningjin/discrete-text-rep
공식 코드Discrete Latent Variable Representations for Low-Resource Text Classification
https://github.com/shunyuzh/MSM
공식 코드Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
https://github.com/shuohangwang/Cross-Thought
공식 코드https://github.com/shuohangwang/SeqMatchSeq
공식 코드https://github.com/shuoyangd/tarsius
공식 코드https://github.com/shushanarakelyan/modular_code_search
공식 코드https://github.com/shyyhs/CourseraParallelCorpusMining
공식 코드Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
https://github.com/si0wang/visvm
공식 코드Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
https://github.com/sidak/context-mover-distance-and-barycenters
공식 코드Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations
https://github.com/siddharthl93/design_kgex
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/siddharthl93/engineering-design-knowledge
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/sigmorphon/2022segmentationst
공식 코드https://github.com/silyrab/aom
공식 코드AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis
https://github.com/simengsun/iga-an-intent-guided-authoring-assistant
공식 코드https://github.com/simone-bombari/attention-sensitivity
공식 코드Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
https://github.com/sinhala-nlp/sold
공식 코드https://github.com/sislab-unitn/new-event-detection
공식 코드Whats New? Identifying the Unfolding of New Events in Narratives
https://github.com/siyuansong2004/language-introspection
공식 코드Language Models Fail to Introspect About Their Knowledge of Language
https://github.com/sjmeis/CLMLDP
공식 코드A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
https://github.com/sjmeis/privfill
공식 코드On the Impact of Noise in Differentially Private Text Rewriting
https://github.com/sjmielke/spell-once
공식 코드Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
https://github.com/sjtu-lit/llmeval_sum_factual
공식 코드Evaluating Factual Consistency of Summaries with Large Language Models
https://github.com/sjtu-lit/syncse
공식 코드https://github.com/skasewa/wronging
공식 코드Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
https://github.com/skp1999/SMAB
공식 코드https://github.com/slanglab-nu/autoencoder-oos
공식 코드https://github.com/slanglab/IndiaPoliceEvents
공식 코드https://github.com/slanglab/transition_sampler
공식 코드Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.