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Open-Domain Dialog

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

1

보존된 평가 기록

21

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yuboxie/meed

공식 코드

A Multi-Turn Emotionally Engaging Dialog Model

https://github.com/AIJoris/DPAC-DialogueGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/CatherineWong/dancin_seq2seq

Adversarial Learning for Neural Dialogue Generation

https://github.com/MindCode-4/code-5/tree/main/xlm_prophetnet

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

https://github.com/PlusLabNLP/PredictiveEngagement

Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems

https://github.com/YufanPaPa/GAN_SSN

Adversarial Learning for Neural Dialogue Generation

https://github.com/aqzheng/Adversarial-Learning-for-Neural-Dialogue-Generation

Adversarial Learning for Neural Dialogue Generation

https://github.com/baber-sos/Explaining-Dialogue-Evaluation-Metrics-using-Adversarial-Behavioral-Analysis

Unsupervised Evaluation of Interactive Dialog with DialoGPT

https://github.com/carriex/lfqa_eval

Hurdles to Progress in Long-form Question Answering

https://github.com/e0397123/dstc10_metric_track

Automatic Evaluation and Moderation of Open-domain Dialogue Systems

https://github.com/emorynlp/gpt-abceval

Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems

https://github.com/facebookresearch/editeval

KILT: a Benchmark for Knowledge Intensive Language Tasks

https://github.com/gmftbyGMFTBY/PONE

PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems

https://github.com/golsun/dialogrpt

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

https://github.com/jsbaan/DPAC-DialogueGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow

Adversarial Learning for Neural Dialogue Generation

https://github.com/orhonovich/q-squared

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

https://github.com/pwc-1/Paper-9/tree/main/1/xlm_prophetnet

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

https://github.com/seungguini/multiref_eval

Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References

https://github.com/smartdataanalytics/proxy_indicators

https://github.com/thomashuang2017/simpson-dialogue-gan-master

Adversarial Learning for Neural Dialogue Generation

https://github.com/thu-coai/OpenMEVA

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

https://github.com/zouharvi/kb-shrink

KILT: a Benchmark for Knowledge Intensive Language Tasks

https://github.com/zpschang/seqGAN

Adversarial Learning for Neural Dialogue Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.