https://github.com/Alab-NII/onecommon
공식 코드A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Papers with Code 아카이브
Language Acquisition
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Language acquisition refers to tasks related to the learning of a second language.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AnonymousReview123/Semantic_Structure_Mapping_Anon
공식 코드Semantic Structure-Mapping in LLM and Human Analogical Reasoning
https://github.com/DannyMerkx/caption2image
공식 코드Learning semantic sentence representations from visually grounded language without lexical knowledge
https://github.com/DannyMerkx/speech2image
공식 코드https://github.com/EvLab-MIT/LexiContrastiveGrd
공식 코드Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
https://github.com/FilipMiscevic/random_walk
공식 코드Predicting and Explaining Human Semantic Search in a Cognitive Model
https://github.com/INK-USC/VG-CCL
공식 코드Visually Grounded Continual Learning of Compositional Phrases
https://github.com/INK-USC/VisCOLL
공식 코드Visually Grounded Continual Learning of Compositional Phrases
https://github.com/JumpyPizza/align-sentence-simplification-with-ESL-learner
공식 코드Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition
https://github.com/MarvinLvn/voice-type-classifier
공식 코드An open-source voice type classifier for child-centered daylong recordings
https://github.com/PaddlePaddle/XWorld
공식 코드Interactive Grounded Language Acquisition and Generalization in a 2D World
https://github.com/SPEECHCOG/ids_audio_quality_analysis
공식 코드https://github.com/Shawn-Guo-CN/EmergentNumerals
공식 코드Emergence of Numeric Concepts in Multi-Agent Autonomous Communication
https://github.com/SonuDixit/gComm
공식 코드https://github.com/aalto-ai/sparse-compgen
공식 코드Compositional Generalization in Grounded Language Learning via Induced Model Sparsity
https://github.com/aaronmueller/emergent-syntax
공식 코드https://github.com/akakzia/decstr
공식 코드Grounding Language to Autonomously-Acquired Skills via Goal Generation
https://github.com/albertqjiang/mma
공식 코드https://github.com/bendiogene/recollection_hypothesis
공식 코드https://github.com/caotians1/BabyAIPlusPlus
공식 코드Zero-Shot Compositional Policy Learning via Language Grounding
https://github.com/clu-uml/ling-cl
공식 코드Ling-CL: Understanding NLP Models through Linguistic Curricula
https://github.com/codebyzeb/Corpus-Phonemizer
공식 코드From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes
https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/fast_mapping
공식 코드https://github.com/dominikglandorf/grammarctg
공식 코드Grammar Control in Dialogue Response Generation for Language Learning Chatbots
https://github.com/ekaterinavoloshina/chronological_probing
공식 코드Is neural language acquisition similar to natural? A chronological probing study
https://github.com/epfl-dlab/broccoli
공식 코드Broccoli: Sprinkling Lightweight Vocabulary Learning into Everyday Information Diets
https://github.com/ericyang1029/language-acquisition
공식 코드https://github.com/feifang24/overmod-from-pragmatic-learning
공식 코드Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning
https://github.com/flowersteam/Imagine
공식 코드Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
https://github.com/flowersteam/playground_env
공식 코드Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
https://github.com/gbegus/articulationgan
공식 코드https://github.com/hjvm/benchmarking_acquisition
공식 코드Evaluating Neural Language Models as Cognitive Models of Language Acquisition
https://github.com/iconstantinescu/lm-critical-period
공식 코드Investigating Critical Period Effects in Language Acquisition through Neural Language Models
https://github.com/ida-szubert/childes_ud2lf
공식 코드Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech
https://github.com/iiscleap/DIHARD_2019_baseline_alltracks
공식 코드The Second DIHARD Diarization Challenge: Dataset, task, and baselines
https://github.com/isi-vista/adam
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.