ACL 2020接收论文列表公开，接收率25.2%，你上榜了吗？

<!-- 政务处理 --> <p data-role="original-title" style="display:none">原标题：ACL 2020接收论文列表公开，接收率25.2%，你上榜了吗？</p> <p style="text-align: center;"><strong>参与：魔王</strong></p> <blockquote> <p>自然语言处理顶会 ACL 2020 将于 7 月 5 日-10 日线上举行。不久之前，ACL 论文接收结果公布，但官方并未放出完整的论文列表。近日，ACL 接收论文列表公布，让我们看一下都有哪些论文被接收了。</p> </blockquote> <p>此次 ACL 会议的投稿数量为 3088 篇，与去年的投稿数量 2906 篇相比稍有增长。ACL 2020 共接收 779 篇论文，包括 571 篇长论文和 208 篇短论文，接收率为 25.2%。</p> <p style="text-align: center;"><img src="/public/uploads/article/2020/05/19/fb0a696e560aed7a9cae0dcd.jpeg" /></p> <p>在接收论文列表中，我们看到了很多熟悉的名字：</p> <p>Christopher D. Manning（斯坦福大学教授、斯坦福 AI 实验室负责人）：</p> <ul> <li>Finding Universal Grammatical Relations in Multilingual BERT</li> <li>Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports</li> <li>Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation</li> </ul> <p>Yoshua Bengio（加拿大计算机科学家、蒙特利尔大学教授）：</p> <ul> <li>Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach</li> </ul> <p>Yoav Goldberg（以色列 Bar-Ilan 大学计算机科学系高级讲师）：</p> <ul> <li>A Formal Hierarchy of RNN Architectures</li> <li>Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection</li> <li>Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora</li> <li>Unsupervised Domain Clusters in Pretrained Language Models</li> <li>A Two-Stage Masked LM Method for Term Set Expansion</li> <li>Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?</li> </ul> <p>Noah A. Smith（华盛顿大学计算机科学与工程系教授）：</p> <ul> <li>A Formal Hierarchy of RNN Architectures</li> <li>A Mixture of h − 1 Heads is Better than h Heads</li> <li>Don't Stop Pretraining: Adapt Language Models to Domains and Tasks</li> <li>Improving Transformer Models by Reordering their Sublayers</li> <li>Social Bias Frames: Reasoning about Social and Power Implications of Language</li> <li>The Right Tool for the Job: Matching Model and Instance Complexities</li> <li>Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models</li> </ul> <p>Percy Liang（斯坦福大学计算机系副教授、斯坦福人工智能实验室成员）：</p> <ul> <li>Robust Encodings: A Framework for Combating Adversarial Typos</li> <li>Selective Question Answering under Domain Shift</li> <li>Enabling Language Models to Fill in the Blanks</li> <li>ExpBERT: Representation Engineering with Natural Language Explanations</li> <li>Shaping Visual Representations with Language for Few-Shot Classification</li> </ul> <p>Sebastian Ruder（DeepMind 研究科学家）：</p> <ul> <li>A Call for More Rigor in Unsupervised Cross-lingual Learning</li> <li>On the Cross-lingual Transferability of Monolingual Representations</li> </ul> <p>周明（微软亚洲研究院副院长、国际计算语言学协会（ACL）主席）：</p> <ul> <li>A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction</li> <li>Curriculum Pre-training for End-to-End Speech Translation</li> <li>Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension</li> <li>Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder</li> <li>Graph Neural News Recommendation with Unsupervised Preference Disentanglement</li> <li>Improving Neural Machine Translation with Soft Template Prediction</li> <li>LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network</li> <li>MIND: A Large-scale Dataset for News Recommendation</li> <li>MuTual: A Dataset for Multi-Turn Dialogue Reasoning</li> <li>Reasoning Over Semantic-Level Graph for Fact Checking</li> <li>A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation</li> <li>A Simple and Effective Unified Encoder for Document-Level Machine Translation</li> </ul> <p>刘铁岩（微软亚洲研究院副院长）：</p> <ul> <li>A Study of Non-autoregressive Model for Sequence Generation</li> <li>SEEK: Segmented Embedding of Knowledge Graphs</li> <li>SimulSpeech: End-to-End Simultaneous Speech to Text Translation</li> </ul> <p>刘群（华为诺亚方舟实验室语音语义首席科学家）：</p> <ul> <li>Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT</li> <li>Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order</li> <li>Word-level Textual Adversarial Attacking as Combinatorial Optimization</li> </ul> <p>宗成庆（中科院自动化所研究员）：</p> <ul> <li>Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization</li> </ul> <p>孙茂松（清华大学计算机科学与技术系教授）：</p> <ul> <li>Continual Relation Learning via Episodic Memory Activation and Reconsolidation</li> <li>Fine-grained Fact Verification with Kernel Graph Attention Network</li> <li>How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence</li> <li>Word-level Textual Adversarial Attacking as Combinatorial Optimization</li> </ul> <p>刘知远（清华大学计算机科学与技术系副教授）：</p> <ul> <li>Continual Relation Learning via Episodic Memory Activation and Reconsolidation</li> <li>Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen</li> <li>Fine-grained Fact Verification with Kernel Graph Attention Network</li> <li>Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs</li> <li>How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence</li> <li>Word-level Textual Adversarial Attacking as Combinatorial Optimization</li> <li>MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs</li> </ul> <p>黄民烈（清华大学计算机科学与技术系副教授）：</p> <ul> <li>A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction</li> <li>KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation</li> <li>Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition</li> </ul> <p>万小军（北京大学计算机科学技术研究所研究员）：</p> <ul> <li>Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study</li> <li>Heterogeneous Graph Transformer for Graph-to-Sequence Learning</li> <li>Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization</li> <li>Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction</li> <li>Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization</li> <li>Semantic Parsing for English as a Second Language</li> <li>Multimodal Transformer for Multimodal Machine Translation</li> </ul> <p>邱锡鹏（复旦大学计算机科学技术学院教授）：</p> <ul> <li>Extractive Summarization as Text Matching</li> <li>Heterogeneous Graph Neural Networks for Extractive Document Summarization</li> <li>Improving Image Captioning with Better Use of Caption</li> <li>FLAT: Chinese NER Using Flat-Lattice Transformer</li> </ul> <p>韩松（MIT 电子工程和计算机科学系助理教授）：</p> <ul> <li>HAT: Hardware-Aware Transformers for Efficient Natural Language Processing</li> </ul> <p>欢迎中了 ACL 2020 论文的读者留言，机器之心也将持续为大家推荐更多优质论文。</p> <p>ACL 2020 接收论文列表，参见：https://acl2020.org/program/accepted/#long-papers</p> <p><strong>本 文为机器之心报道， 转载请联系本公众号获得授权 。</strong><i class="backsohu"></i>返回搜狐，查看更多</p> <!-- 政务账号添加来源标示处理 --> <!-- 政务账号添加来源标示处理 --> <p data-role="editor-name">责任编辑：</p>