https://github.com/haodeqi/BenchmarkingIntentDetection
Benchmarking Natural Language Understanding Services for building Conversational Agents
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
https://github.com/harshit158/paper-dots
https://github.com/harvardnlp/annotated-transformer
https://github.com/hate-alert/indicabusive
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/haydlite/sparse-bert-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hegebharat/SelfDiscovery
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hegebharat/sentiment-Analysis-for-German-Datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/helboukkouri/character-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/heraclex12/vietpunc
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hfxunlp/transformer
https://github.com/hiejulia/data-mining
https://github.com/hiun/learning-transformers
https://github.com/hkunlp/icl-ceil
https://github.com/hlt-mt/speech-massive
https://github.com/hltcoe/colbert-x
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
https://github.com/hoichunlaw/COVID-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/holgerbovbjerg/data2vec-kws
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/hrbigelow/transformer-aiayn
https://github.com/huanghonggit/Mask-Language-Model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/huberemanuel/DeBERTa
https://github.com/hubiodatalab/druggen
https://github.com/huggingface/transformers/tree/main/src/transformers/models/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/huulinhcvp/chatBot
https://github.com/huynhduchuydp36/VLSP2019-SHARED-Task-Hate-Speech-Detection-on-Social-Networks-Using-Bi-Lstm
Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/iai-group/moviebot
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idc-dsi/Arapet
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
https://github.com/idiap/bert-text-diarization-atc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.