{"list":"other","total":53,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"id":"1904.09675","title":"BERTScore: Evaluating Text Generation with BERT","created":"2019-04-21","cited_by_count":5512},{"id":"2004.04906","title":"Dense Passage Retrieval for Open-Domain Question Answering","created":"2020-04-10","cited_by_count":3824},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1904.09751","title":"The Curious Case of Neural Text Degeneration","created":"2019-04-22","cited_by_count":2916},{"id":"1911.00536","title":"DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation","created":"2019-11-01","cited_by_count":842},{"id":"1801.07243","title":"Personalizing Dialogue Agents: I have a dog, do you have pets too?","created":"2018-01-22","cited_by_count":828},{"id":"1908.07125","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","created":"2019-08-20","cited_by_count":722},{"id":"1907.11932","title":"Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment","created":"2019-07-27","cited_by_count":671},{"id":"1710.03957","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","created":"2017-10-11","cited_by_count":652},{"id":"2004.13637","title":"Recipes for building an open-domain chatbot","created":"2020-04-28","cited_by_count":593},{"id":"1910.12840","title":"Evaluating the Factual Consistency of Abstractive Text Summarization","created":"2019-10-28","cited_by_count":584},{"id":"1707.07328","title":"Adversarial Examples for Evaluating Reading Comprehension Systems","created":"2017-07-23","cited_by_count":571},{"id":"2001.09977","title":"Towards a Human-like Open-Domain Chatbot","created":"2020-01-27","cited_by_count":470},{"id":"1909.12434","title":"Learning the Difference that Makes a Difference with Counterfactually-Augmented Data","created":"2019-09-26","cited_by_count":439},{"id":"1804.07998","title":"Generating Natural Language Adversarial Examples","created":"2018-04-21","cited_by_count":424},{"id":"1603.08023","title":"How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation","created":"2016-03-25","cited_by_count":356},{"id":"1506.06726","title":"Skip-Thought Vectors","created":"2015-06-22","cited_by_count":322},{"id":"2104.13346","title":"Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics","created":"2021-04-27","cited_by_count":287},{"id":"1804.06059","title":"Adversarial Example Generation with Syntactically Controlled Paraphrase Networks","created":"2018-04-17","cited_by_count":286},{"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":"1901.08149","title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","created":"2019-01-23","cited_by_count":188},{"id":"2005.00456","title":"USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation","created":"2020-05-01","cited_by_count":188},{"id":"1605.06069","title":"A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues","created":"2016-05-19","cited_by_count":174},{"id":"1905.01969","title":"Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring","created":"2019-04-22","cited_by_count":165},{"id":"1905.11268","title":"Combating Adversarial Misspellings with Robust Word Recognition","created":"2019-05-27","cited_by_count":163},{"id":"1809.10610","title":"Counterfactual Fairness in Text Classification through Robustness","created":"2018-09-27","cited_by_count":151},{"id":"1911.03064","title":"Reducing Sentiment Bias in Language Models via Counterfactual Evaluation","created":"2019-11-08","cited_by_count":148},{"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":"2005.05339","title":"Enabling Language Models to Fill in the Blanks","created":"2020-05-11","cited_by_count":122},{"id":"2012.13985","title":"Explaining NLP Models via Minimal Contrastive Editing (MiCE)","created":"2020-12-27","cited_by_count":106},{"id":"1708.07149","title":"Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses","created":"2017-08-23","cited_by_count":98},{"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":"2012.04698","title":"Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text","created":"2020-12-08","cited_by_count":75},{"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":"2007.06661","title":"Robustness to Spurious Correlations via Human Annotations","created":"2020-07-13","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":"1904.10635","title":"Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings","created":"2019-04-24","cited_by_count":52},{"id":"2007.06174","title":"Generating Fluent Adversarial Examples for Natural Languages","created":"2020-07-13","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":"2011.04743","title":"Adversarial Semantic Collisions","created":"2020-11-09","cited_by_count":42},{"id":"2004.04908","title":"Designing Precise and Robust Dialogue Response Evaluators","created":"2020-04-10","cited_by_count":37},{"id":"2005.04315","title":"Probing Linguistic Systematicity","created":"2020-05-08","cited_by_count":37},{"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":"1904.13015","title":"Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators","created":"2019-04-30","cited_by_count":35},{"id":"2008.09075","title":"Controlling Dialogue Generation with Semantic Exemplars","created":"2020-08-20","cited_by_count":25},{"id":"1708.06816","title":"Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs","created":"2017-08-22","cited_by_count":23},{"id":"1710.11344","title":"A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots","created":"2017-10-31","cited_by_count":9},{"id":"2004.14302","title":"Evaluating Dialogue Generation Systems via Response Selection","created":"2020-04-29","cited_by_count":9},{"id":"1801.01632","title":"VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling","created":"2018-01-05","cited_by_count":3},{"id":"2009.07117","title":"Multi-Referenced Training for Dialogue Response Generation","created":"2020-09-15","cited_by_count":3}],"next_cursor":null}