https://github.com/abbey4799/hauser
공식 코드HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
Papers with Code 아카이브
Dialogue
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Dialogue is notoriously hard to evaluate. Past approaches have used human evaluation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/adamlin120/cldst
공식 코드An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker
https://github.com/afsara-ben/speechcache
공식 코드https://github.com/akreal/bloomzmms
공식 코드https://github.com/akshittyagi/intclass
공식 코드Fast Intent Classification for Spoken Language Understanding
https://github.com/ala-na/media_benchmark_intent_annotations
공식 코드New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
https://github.com/alanbaade/SyllableLM
공식 코드SyllableLM: Learning Coarse Semantic Units for Speech Language Models
https://github.com/alcein/taira
공식 코드Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
https://github.com/alexa/alexa-dataset-contextual-query-rewrite
공식 코드A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
https://github.com/alexa/alexa-with-dstc9-track1-dataset
공식 코드https://github.com/alexa/dstqa
공식 코드Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
https://github.com/alexa/teach
공식 코드https://github.com/algoprog/syntod
공식 코드Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
https://github.com/alibaba-nlp/aishell-ner
공식 코드https://github.com/alibaba/esim-response-selection
공식 코드Sequential Attention-based Network for Noetic End-to-End Response Selection
https://github.com/alon-albalak/tlidb
공식 코드FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
https://github.com/alumae/torch-xvectors-wav
공식 코드https://github.com/amazon-research/dialogue-meaning-representation
공식 코드Dialogue Meaning Representation for Task-Oriented Dialogue Systems
https://github.com/amazon-research/domain-knowledge-injection
공식 코드Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
https://github.com/amazon-research/dse
공식 코드Learning Dialogue Representations from Consecutive Utterances
https://github.com/amazon-research/multiatis
공식 코드End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
https://github.com/amazon-research/read-up
공식 코드Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation
https://github.com/amazon-research/real-world-noisy-benchmarks-for-natural-language-understanding
공식 코드https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amazon-science/dstc11-track2-intent-induction
공식 코드https://github.com/amazon-science/efficient-dialogue-state-tracking-by-sequential-information-processing
공식 코드Diable: Efficient Dialogue State Tracking as Operations on Tables
https://github.com/amazon-science/intent-aware-encoder
공식 코드Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
https://github.com/amazon-science/zero-shot-e2e-slu
공식 코드Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
https://github.com/amphionspace/sd-eval
공식 코드SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words
https://github.com/amzn/user-satisfaction-modeling
공식 코드Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
https://github.com/andreamad8/FSB
공식 코드https://github.com/andreamad8/ToDCL
공식 코드https://github.com/andy194673/Joint-NLU-NLG
공식 코드A Generative Model for Joint Natural Language Understanding and Generation
https://github.com/anhtunguyen98/bislu
공식 코드https://github.com/ankitapasad/layerwise-analysis
공식 코드Comparative layer-wise analysis of self-supervised speech models
https://github.com/anonymous-1759/eqhard-em
공식 코드An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.