包括微软、CMU、Stanford在内的顶级人工智能专家和学者们正在研究更复杂的任务:让机器像人类一样阅读文本,进而根据对该文本的理解来回答问题。这种阅读理解就像是让计算机来做我们高考英语的阅读理解题。

知识荟萃

机器阅读理解(Reading comprehension)专知荟萃

入门学习

  1. 深度学习解决机器阅读理解任务的研究进展 张俊林
  2. 从短句到长文,计算机如何学习阅读理解 微软亚洲研究院
  3. 基于深度学习的阅读理解 冯岩松
  4. SQuAD综述
  5. 教机器学习阅读 张俊
  6. 解读DeepMind的论文“教会机器阅读和理解”
  7. 机器阅读理解中文章和问题的深度学习表示方法

综述

  1. Emergent Logical Structure in Vector Representations of Neural Readers
  2. 机器阅读理解任务综述 林鸿宇 韩先培

进阶论文

  1. Teaching Machines to Read and Comprehend
  2. Learning to Ask: Neural Question Generation for Reading Comprehension
  3. Attention-over-Attention Neural Networks for Reading Comprehension
  4. R-NET: MACHINE READING COMPREHENSION WITH SELF-MATCHING NETWORKS
  5. Mnemonic Reader for Machine Comprehension
  6. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
  7. S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
  8. RACE: Large-scale ReAding Comprehension Dataset From Examinations
  9. Adversarial Examples for Evaluating Reading Comprehension Systems
  10. Machine comprehension using match-lstm and answer pointer
  11. Multi-perspective context matching for machine comprehension
  12. Reasonet: Learning to stop reading in machine comprehension
  13. Learning recurrent span representations for extractive question answering
  14. End-to-end answer chunk extraction and ranking for reading comprehension
  15. Words or characters? fine-grained gating for reading comprehension
  16. Reading Wikipedia to Answer Open-Domain Questions
  17. An analysis of prerequisite skills for reading comprehension
  18. A Comparative Study of Word Embeddings for Reading Comprehension

Datasets

  1. MCTest
  2. bAbI
  3. WikiQA
  4. SNLI
  5. Children's Book Test
  6. BookTest
  7. CNN / Daily Mail
  8. Who Did What
  9. NewsQA
  10. SQuAD
  11. LAMBADA
  12. MS MARCO
  13. WikiMovies
  14. WikiReading

Code

  1. CNN/Daily Mail Reading Comprehension Task
  2. TriviaQA
  3. Attentive Reader
  4. DrQA

领域专家

  1.  Percy Liang
  2. 刘挺
  3. Jason Weston

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VIP内容

机器阅读理解(MRC)是一个受数据集推动的研究领域,其目标是让机器在理解文章内容的基础上能够正确回答相关问题。早期受数据集限制,机器阅读理解任务大多局限于单轮问答,问答对之间缺少依赖关系。而会话问答(ConvQA)是使机器在帮助人类获取信息时可以进行连续主题的人机交互过程。近年来,随着机器阅读理解数据集和深度神经网络的发展,研究人员将机器阅读理解与会话问答结合,形成更为复杂真实的会话式机器阅读理解(CMC),这极大地推动了机器阅读理解领域的发展。对近几年会话式机器阅读理解相关最新研究进展从三方面归纳总结:首先阐述该任务的定义、所面临的挑战以及相关数据集的特性;然后归纳总结当前最新模型的架构及其研究进展,着重介绍会话历史嵌入表示以及会话推理所使用的相关技术方法;最后梳理分析当前会话式机器阅读理解模型,并对未来研究重点和研究方法进行展望。

http://fcst.ceaj.org/CN/abstract/abstract2875.shtml

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最新论文

We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when answer entities have different names? Such failures would indicate that models are overly reliant on entity knowledge to answer questions, and therefore may generalize poorly when facts about the world change or questions are asked about novel entities. To systematically audit model robustness, we propose a general and scalable method to replace person names with names from a variety of sources, ranging from common English names to names from other languages to arbitrary strings. Across four datasets and three pretrained model architectures, MRC models consistently perform worse when entities are renamed, with particularly large accuracy drops on datasets constructed via distant supervision. We also find large differences between models: SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa, despite having similar accuracy on unperturbed test data. Inspired by this, we experiment with span-level and entity-level masking as a continual pretraining objective and find that they can further improve the robustness of MRC models.

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