https://github.com/AI4Bharat/indicSUPERB
공식 코드IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Language identification is the task of determining the language of a text.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
A reproduction of Apple's bi-directional LSTM models for language identification in short strings
Universal Dependency Parsing for Hindi-English Code-switching
KréyoLID From Language Identification Towards Language Mining
Language Identification Using Deep Convolutional Recurrent Neural Networks
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices
Enhance Language Identification using Dual-mode Model with Knowledge Distillation
From English to Code-Switching: Transfer Learning with Strong Morphological Clues
Topics to Avoid: Demoting Latent Confounds in Text Classification
A Benchmark Study of Contrastive Learning for Arabic Social Meaning
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media
Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts
GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
MaskLID: Code-Switching Language Identification through Iterative Masking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.