https://github.com/miulab/convadr-qa
공식 코드Open-Domain Conversational Question Answering with Historical Answers
Papers with Code 아카이브
Conversational Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nusnlp/diffqre
공식 코드On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
https://github.com/orange-opensource/coqar
공식 코드https://github.com/prdwb/attentive_history_selection
공식 코드Attentive History Selection for Conversational Question Answering
https://github.com/prdwb/bert_hae
공식 코드BERT with History Answer Embedding for Conversational Question Answering
https://github.com/prdwb/orconvqa-release
공식 코드https://github.com/prdwb/ws-orconvqa
공식 코드Weakly-Supervised Open-Retrieval Conversational Question Answering
https://github.com/princeton-nlp/cqa-evaluation
공식 코드Ditch the Gold Standard: Re-evaluating Conversational Question Answering
https://github.com/princeton-nlp/evalconvqa
공식 코드Ditch the Gold Standard: Re-evaluating Conversational Question Answering
https://github.com/scai-conf/scai-qrecc-21
공식 코드https://github.com/searchableai/ChainCQG
공식 코드https://github.com/sebischair/llm-sp-cqa
공식 코드https://github.com/seonjeonghwang/e2eqr
공식 코드https://github.com/shihabrashid-ucr/normy
공식 코드NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering
https://github.com/srsawant34/efficient_instruction_learning
공식 코드https://github.com/starsuzi/as-convqa
공식 코드https://github.com/starsuzi/pro-convqa
공식 코드https://github.com/svakulenk0/QRQA
공식 코드https://github.com/svakulenk0/cast_evaluation
공식 코드A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
https://github.com/taoshen58/MaSP
공식 코드Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
https://github.com/tttttttty/alcqa
공식 코드https://github.com/wengsyx/hot
공식 코드Large Language Models Need Holistically Thought in Medical Conversational QA
https://github.com/wengsyx/lingyi
공식 코드LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
https://github.com/xdao85/vnhsge
공식 코드VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
https://github.com/zahraabbasiantaeb/simquac
공식 코드Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions
https://github.com/zorikg/marcqap
공식 코드https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sdne
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-4/Code6/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/McGill-NLP/topiocqa
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
https://github.com/MindCode-4/code-12/tree/main/model-conversion-via
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/MindSpore-paper-code-3/code10/tree/main/guided_anchoring
https://github.com/MindSpore-scientific/code-6/tree/main/multi-adversarial-domain-adaptation
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/dialogue/plato-2
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
https://github.com/alibaba/EasyTransfer
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
https://github.com/code-implementation1/Code7/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/egillian1/AblationPLATO
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.