{"list":"other","total":58,"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":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1508.07909","title":"Neural Machine Translation of Rare Words with Subword Units","created":"2015-08-31","cited_by_count":2918},{"id":"1802.05365","title":"Deep contextualized word representations","created":"2018-02-15","cited_by_count":2290},{"id":"2104.08821","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","created":"2021-04-18","cited_by_count":2173},{"id":"2004.10964","title":"Don't Stop Pretraining: Adapt Language Models to Domains and Tasks","created":"2020-04-23","cited_by_count":1565},{"id":"1911.00536","title":"DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation","created":"2019-11-01","cited_by_count":842},{"id":"1810.00278","title":"MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling","created":"2018-09-29","cited_by_count":766},{"id":"1710.03957","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","created":"2017-10-11","cited_by_count":652},{"id":"1707.07328","title":"Adversarial Examples for Evaluating Reading Comprehension Systems","created":"2017-07-23","cited_by_count":571},{"id":"1811.01241","title":"Wizard of Wikipedia: Knowledge-Powered Conversational agents","created":"2018-11-03","cited_by_count":530},{"id":"1909.12434","title":"Learning the Difference that Makes a Difference with Counterfactually-Augmented Data","created":"2019-09-26","cited_by_count":439},{"id":"2003.04807","title":"Efficient Intent Detection with Dual Sentence Encoders","created":"2020-03-10","cited_by_count":433},{"id":"1909.02027","title":"An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction","created":"2019-09-04","cited_by_count":386},{"id":"1611.01734","title":"Deep Biaffine Attention for Neural Dependency Parsing","created":"2016-11-06","cited_by_count":334},{"id":"1604.04562","title":"A Network-based End-to-End Trainable Task-oriented Dialogue System","created":"2016-04-15","cited_by_count":252},{"id":"1907.04307","title":"Multilingual Universal Sentence Encoder for Semantic Retrieval","created":"2019-07-09","cited_by_count":193},{"id":"2004.06871","title":"TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue","created":"2020-04-15","cited_by_count":185},{"id":"2105.06762","title":"DialogSum: A Real-Life Scenario Dialogue Summarization Dataset","created":"2021-05-14","cited_by_count":183},{"id":"1903.05566","title":"Benchmarking Natural Language Understanding Services for building Conversational Agents","created":"2019-03-13","cited_by_count":166},{"id":"1711.01731","title":"A Survey on Dialogue Systems: Recent Advances and New Frontiers","created":"2017-11-06","cited_by_count":162},{"id":"1910.07931","title":"PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable","created":"2019-10-17","cited_by_count":145},{"id":"1508.01745","title":"Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems","created":"2015-08-07","cited_by_count":134},{"id":"2005.02877","title":"TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking","created":"2020-05-06","cited_by_count":130},{"id":"1905.04071","title":"Survey on Evaluation Methods for Dialogue Systems","created":"2019-05-10","cited_by_count":126},{"id":"2101.09459","title":"Advances and Challenges in Conversational Recommender Systems: A Survey","created":"2021-01-23","cited_by_count":118},{"id":"2004.04494","title":"MuTual: A Dataset for Multi-Turn Dialogue Reasoning","created":"2020-04-09","cited_by_count":115},{"id":"1810.07942","title":"Semantic Parsing for Task Oriented Dialog using Hierarchical Representations","created":"2018-10-18","cited_by_count":101},{"id":"1902.01889","title":"Analyzing and Improving Representations with the Soft Nearest Neighbor Loss","created":"2019-02-05","cited_by_count":95},{"id":"1911.03688","title":"ConveRT: Efficient and Accurate Conversational Representations from Transformers","created":"2019-11-09","cited_by_count":95},{"id":"2009.13570","title":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","created":"2020-09-28","cited_by_count":89},{"id":"2109.02492","title":"DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization","created":"2021-09-06","cited_by_count":72},{"id":"2004.08056","title":"Dialogue-Based Relation Extraction","created":"2020-04-17","cited_by_count":68},{"id":"1805.05492","title":"Did the Model Understand the Question?","created":"2018-05-14","cited_by_count":60},{"id":"1704.08381","title":"Neural AMR: Sequence-to-Sequence Models for Parsing and Generation","created":"2017-04-26","cited_by_count":56},{"id":"1805.02473","title":"A Graph-to-Sequence Model for AMR-to-Text Generation","created":"2018-05-07","cited_by_count":56},{"id":"2203.07836","title":"Graph Pre-training for AMR Parsing and Generation","created":"2022-03-15","cited_by_count":55},{"id":"2103.09120","title":"Structural Adapters in Pretrained Language Models for AMR-to-text Generation","created":"2021-03-16","cited_by_count":48},{"id":"2004.05572","title":"AMR Parsing via Graph-Sequence Iterative Inference","created":"2020-04-12","cited_by_count":47},{"id":"1904.06472","title":"A Repository of Conversational Datasets","created":"2019-04-13","cited_by_count":45},{"id":"2005.08866","title":"Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations","created":"2020-05-18","cited_by_count":42},{"id":"2010.12770","title":"Conversational Semantic Parsing for Dialog State Tracking","created":"2020-10-24","cited_by_count":40},{"id":"2009.13655","title":"Conversational Semantic Parsing","created":"2020-09-28","cited_by_count":39},{"id":"1805.05286","title":"AMR Parsing as Graph Prediction with Latent Alignment","created":"2018-05-14","cited_by_count":36},{"id":"2106.01541","title":"MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding","created":"2021-06-03","cited_by_count":36},{"id":"1909.00136","title":"Modeling Graph Structure in Transformer for Better AMR-to-Text Generation","created":"2019-08-31","cited_by_count":35},{"id":"1909.02607","title":"Broad-Coverage Semantic Parsing as Transduction","created":"2019-09-05","cited_by_count":32},{"id":"2012.06780","title":"GDPNet: Refining Latent Multi-View Graph for Relation Extraction","created":"2020-12-12","cited_by_count":29},{"id":"2109.04008","title":"Graph Based Network with Contextualized Representations of Turns in Dialogue","created":"2021-09-09","cited_by_count":26},{"id":"2105.10188","title":"Semantic Representation for Dialogue Modeling","created":"2021-05-21","cited_by_count":24},{"id":"1903.05164","title":"Scaling Multi-Domain Dialogue State Tracking via Query Reformulation","created":"2019-03-12","cited_by_count":20},{"id":"2105.10956","title":"Structural Pre-training for Dialogue Comprehension","created":"2021-05-23","cited_by_count":20},{"id":"2109.05487","title":"Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation","created":"2021-09-12","cited_by_count":13},{"id":"2010.04520","title":"Online Back-Parsing for AMR-to-Text Generation","created":"2020-10-09","cited_by_count":9},{"id":"2205.00379","title":"The Cross-lingual Conversation Summarization Challenge","created":"2022-05-01","cited_by_count":8},{"id":"2208.08678","title":"Mere Contrastive Learning for Cross-Domain Sentiment Analysis","created":"2022-08-18","cited_by_count":8}],"next_cursor":null}