{"list":"other","total":55,"rows":[{"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":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1702.08734","title":"Billion-scale similarity search with GPUs","created":"2017-02-28","cited_by_count":2497},{"id":"1611.09268","title":"MS MARCO: A Human Generated MAchine Reading COmprehension Dataset","created":"2016-11-28","cited_by_count":1605},{"id":"2104.08663","title":"BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models","created":"2021-04-17","cited_by_count":1252},{"id":"1510.03055","title":"A Diversity-Promoting Objective Function for Neural Conversation Models","created":"2015-10-11","cited_by_count":1077},{"id":"2004.09297","title":"MPNet: Masked and Permuted Pre-training for Language Understanding","created":"2020-04-20","cited_by_count":981},{"id":"2007.00808","title":"Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval","created":"2020-07-01","cited_by_count":849},{"id":"1911.00536","title":"DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation","created":"2019-11-01","cited_by_count":842},{"id":"1901.04085","title":"Passage Re-ranking with BERT","created":"2019-01-13","cited_by_count":645},{"id":"2010.04592","title":"Contrastive Learning with Hard Negative Samples","created":"2020-10-09","cited_by_count":628},{"id":"2010.08191","title":"RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering","created":"2020-10-16","cited_by_count":413},{"id":"2003.07820","title":"Overview of the TREC 2019 deep learning track","created":"2020-03-17","cited_by_count":286},{"id":"2010.06467","title":"Pretrained Transformers for Text Ranking: BERT and Beyond","created":"2020-10-13","cited_by_count":267},{"id":"2104.06967","title":"Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling","created":"2021-04-14","cited_by_count":238},{"id":"1603.06155","title":"A Persona-Based Neural Conversation Model","created":"2016-03-19","cited_by_count":223},{"id":"2108.05540","title":"Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval","created":"2021-08-12","cited_by_count":203},{"id":"2101.11038","title":"Muppet: Massive Multi-task Representations with Pre-Finetuning","created":"2021-01-26","cited_by_count":192},{"id":"1506.08909","title":"The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems","created":"2015-06-30","cited_by_count":172},{"id":"2104.08051","title":"Optimizing Dense Retrieval Model Training with Hard Negatives","created":"2021-04-16","cited_by_count":155},{"id":"2010.08240","title":"Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks","created":"2020-10-16","cited_by_count":138},{"id":"2109.10086","title":"SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval","created":"2021-09-21","cited_by_count":127},{"id":"1809.01984","title":"Training Millions of Personalized Dialogue Agents","created":"2018-09-06","cited_by_count":126},{"id":"2104.07186","title":"COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List","created":"2021-04-15","cited_by_count":113},{"id":"1612.01627","title":"Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots","created":"2016-12-06","cited_by_count":103},{"id":"1911.03688","title":"ConveRT: Efficient and Accurate Conversational Representations from Transformers","created":"2019-11-09","cited_by_count":95},{"id":"2004.03588","title":"Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots","created":"2020-04-07","cited_by_count":73},{"id":"2005.04246","title":"ConvoKit: A Toolkit for the Analysis of Conversations","created":"2020-05-08","cited_by_count":72},{"id":"2104.12016","title":"Learning Passage Impacts for Inverted Indexes","created":"2021-04-24","cited_by_count":68},{"id":"2005.09863","title":"Understanding Negative Sampling in Graph Representation Learning","created":"2020-05-20","cited_by_count":66},{"id":"1910.10687","title":"Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval","created":"2019-10-23","cited_by_count":65},{"id":"2101.08751","title":"Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline","created":"2021-01-21","cited_by_count":65},{"id":"2101.03305","title":"LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification","created":"2021-01-09","cited_by_count":61},{"id":"2107.13602","title":"Domain-matched Pre-training Tasks for Dense Retrieval","created":"2021-07-28","cited_by_count":53},{"id":"2108.06027","title":"PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval","created":"2021-08-13","cited_by_count":50},{"id":"2201.05176","title":"Neural Approaches to Conversational Information Retrieval","created":"2022-01-13","cited_by_count":42},{"id":"2201.08808","title":"Conversational Information Seeking","created":"2022-01-21","cited_by_count":37},{"id":"1804.08759","title":"Analyzing and Characterizing User Intent in Information-seeking Conversations","created":"2018-04-23","cited_by_count":36},{"id":"2009.04703","title":"Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection","created":"2020-09-10","cited_by_count":36},{"id":"2009.06265","title":"Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues","created":"2020-09-14","cited_by_count":34},{"id":"2012.14756","title":"Dialogue Response Selection with Hierarchical Curriculum Learning","created":"2020-12-29","cited_by_count":27},{"id":"1912.04639","title":"Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset","created":"2019-12-10","cited_by_count":26},{"id":"2110.01529","title":"A Proposed Conceptual Framework for a Representational Approach to Information Retrieval","created":"2021-10-04","cited_by_count":24},{"id":"2105.10956","title":"Structural Pre-training for Dialogue Comprehension","created":"2021-05-23","cited_by_count":20},{"id":"2108.08513","title":"Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion","created":"2021-08-19","cited_by_count":20},{"id":"1805.00188","title":"Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems","created":"2018-05-01","cited_by_count":18},{"id":"2002.00571","title":"IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems","created":"2020-02-03","cited_by_count":16},{"id":"1911.06940","title":"Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots","created":"2019-11-16","cited_by_count":13},{"id":"1510.03753","title":"Improved Deep Learning Baselines for Ubuntu Corpus Dialogs","created":"2015-10-13","cited_by_count":12},{"id":"2103.03125","title":"Advances in Multi-turn Dialogue Comprehension: A Survey","created":"2021-03-04","cited_by_count":12},{"id":"2110.06612","title":"Exploring Dense Retrieval for Dialogue Response Selection","created":"2021-10-13","cited_by_count":11},{"id":"2109.06538","title":"Challenging Instances are Worth Learning: Generating Valuable Negative Samples for Response Selection Training","created":"2021-09-14","cited_by_count":3},{"id":"2110.00159","title":"Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning","created":"2021-10-01","cited_by_count":3}],"next_cursor":null}