{"list":"other","total":54,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1606.05250","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","created":"2016-06-16","cited_by_count":4148},{"id":"2109.01652","title":"Finetuned Language Models Are Zero-Shot Learners","created":"2021-09-03","cited_by_count":3780},{"id":"2004.04696","title":"BLEURT: Learning Robust Metrics for Text Generation","created":"2020-04-09","cited_by_count":1203},{"id":"2103.10360","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","created":"2021-03-18","cited_by_count":1031},{"id":"2106.11520","title":"BARTScore: Evaluating Generated Text as Text Generation","created":"2021-06-22","cited_by_count":722},{"id":"2110.07602","title":"P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks","created":"2021-10-14","cited_by_count":689},{"id":"1710.03957","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","created":"2017-10-11","cited_by_count":652},{"id":"1904.09223","title":"ERNIE: Enhanced Representation through Knowledge Integration","created":"2019-04-19","cited_by_count":302},{"id":"2107.02137","title":"ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","created":"2021-07-05","cited_by_count":260},{"id":"1703.10960","title":"Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders","created":"2017-03-31","cited_by_count":222},{"id":"1507.04808","title":"Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models","created":"2015-07-17","cited_by_count":207},{"id":"2005.00456","title":"USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation","created":"2020-05-01","cited_by_count":188},{"id":"1905.05709","title":"Challenges in Building Intelligent Open-domain Dialog Systems","created":"2019-05-13","cited_by_count":147},{"id":"2006.12719","title":"Unsupervised Evaluation of Interactive Dialog with DialoGPT","created":"2020-06-23","cited_by_count":139},{"id":"2004.08449","title":"Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills","created":"2020-04-17","cited_by_count":131},{"id":"2105.04387","title":"Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey","created":"2021-05-10","cited_by_count":130},{"id":"1905.04071","title":"Survey on Evaluation Methods for Dialogue Systems","created":"2019-05-10","cited_by_count":126},{"id":"1811.10996","title":"CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling","created":"2018-11-14","cited_by_count":94},{"id":"2308.11995","title":"Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations","created":"2023-08-23","cited_by_count":89},{"id":"1701.03079","title":"RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems","created":"2017-01-11","cited_by_count":88},{"id":"1801.03604","title":"Conversational AI: The Science Behind the Alexa Prize","created":"2018-01-11","cited_by_count":63},{"id":"2201.04723","title":"Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents","created":"2022-01-12","cited_by_count":62},{"id":"2009.11321","title":"Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining","created":"2020-09-23","cited_by_count":59},{"id":"1703.01008","title":"End-to-End Task-Completion Neural Dialogue Systems","created":"2017-03-03","cited_by_count":52},{"id":"2005.00583","title":"Learning an Unreferenced Metric for Online Dialogue Evaluation","created":"2020-05-01","cited_by_count":52},{"id":"2005.00613","title":"A Controllable Model of Grounded Response Generation","created":"2020-05-01","cited_by_count":49},{"id":"1907.10568","title":"Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References","created":"2019-07-24","cited_by_count":48},{"id":"2006.08732","title":"Evaluating Conversational Recommender Systems via User Simulation","created":"2020-06-15","cited_by_count":47},{"id":"2011.00483","title":"Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems","created":"2020-11-01","cited_by_count":47},{"id":"2105.03748","title":"Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems","created":"2021-05-08","cited_by_count":47},{"id":"2110.06696","title":"Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese","created":"2021-10-13","cited_by_count":45},{"id":"2004.07672","title":"Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation","created":"2020-04-16","cited_by_count":41},{"id":"2010.12770","title":"Conversational Semantic Parsing for Dialog State Tracking","created":"2020-10-24","cited_by_count":40},{"id":"2004.04908","title":"Designing Precise and Robust Dialogue Response Evaluators","created":"2020-04-10","cited_by_count":37},{"id":"2006.06110","title":"Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols","created":"2020-06-10","cited_by_count":35},{"id":"2103.02548","title":"NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation","created":"2021-03-03","cited_by_count":35},{"id":"1908.09456","title":"Attentive History Selection for Conversational Question Answering","created":"2019-08-26","cited_by_count":33},{"id":"1702.03274","title":"Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning","created":"2017-02-10","cited_by_count":32},{"id":"2109.04137","title":"Fusing task-oriented and open-domain dialogues in conversational agents","created":"2021-09-09","cited_by_count":28},{"id":"2010.02140","title":"Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems","created":"2020-10-05","cited_by_count":26},{"id":"2205.00176","title":"Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models","created":"2022-04-30","cited_by_count":26},{"id":"2203.09711","title":"DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations","created":"2022-03-18","cited_by_count":25},{"id":"2005.10716","title":"Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation","created":"2020-05-21","cited_by_count":22},{"id":"2004.14307","title":"UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues","created":"2020-04-29","cited_by_count":19},{"id":"2010.01893","title":"Regularizing Dialogue Generation by Imitating Implicit Scenarios","created":"2020-10-05","cited_by_count":16},{"id":"1911.08567","title":"Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation","created":"2019-11-18","cited_by_count":12},{"id":"1906.04991","title":"Incremental Learning from Scratch for Task-Oriented Dialogue Systems","created":"2019-06-12","cited_by_count":11},{"id":"2004.14302","title":"Evaluating Dialogue Generation Systems via Response Selection","created":"2020-04-29","cited_by_count":9},{"id":"1906.00549","title":"Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment","created":"2019-06-03","cited_by_count":8},{"id":"2301.13372","title":"Improving Open-Domain Dialogue Evaluation with a Causal Inference Model","created":"2023-01-31","cited_by_count":4}],"next_cursor":null}