Papers with Code 아카이브
Intent Classification
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Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents
Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PolyAI-LDN/polyai-models
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/PolyAI-LDN/task-specific-datasets
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/RasaHQ/rasa
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/RyanGoslingsBugle/priv-text
CAPE: Context-Aware Private Embeddings for Private Language Learning
https://github.com/ShawonAshraf/nlu-jointbert-dl2021
https://github.com/VinAIResearch/JointIDSF
https://github.com/ai4bharat/indicbert
https://github.com/alibaba-damo-academy/spokennlp
https://github.com/amazon-science/multiatis
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
https://github.com/asadovsky/nn
https://github.com/bhchoi/bert-for-joint-ic-sf
https://github.com/chimechallenge/C8DASR-Baseline-NeMo
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
https://github.com/congyingxia/IncrementalFSTC
https://github.com/davidalami/convert
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/dsindex/iclassifier
https://github.com/farbodtaymouri/BERT-GAN
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/freedomintelligence/sdak
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
https://github.com/golsun/dialogrpt
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/haodeqi/BenchmarkingIntentDetection
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/hlt-mt/speech-massive
https://github.com/hongshengxin/Induction_network
https://github.com/jordiclive/Convert-PolyAI-Torch
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/kakaoenterprise/OutFlip
OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack
https://github.com/kehanlu/Nemo
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
https://github.com/koujm/convert-tf
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/lackel/hierarchical_weighted_scl
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/lackel/sdc
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/laohur/LearnToCompareText
https://github.com/laohur/RelationNet
https://github.com/lefteris12/multitask-schema-guided-dst
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
https://github.com/mangushev/intent_slot
https://github.com/martinambition/IntentClassificationBenchmark
Subword Semantic Hashing for Intent Classification on Small Datasets
https://github.com/mhw32/prototransformer-public
https://github.com/mindslab-ai/pnlp-mixer
https://github.com/monologg/JointBERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.