智能问答（Question Answering）的主要研究方向

怼烎@ 2023-09-28 15:54 9阅读 0赞

非事实类问题

大多数研究关注于事实类问题，而非事实类问题的研究相对不足，包括数学类的问题、判断类的问题等。

\[EMNLP 2019\] NumNet: Machine Reading Comprehension with Numerical Reasoning 数学类问题

\[NAACL19\] MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

\[NAACL19\] BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

多跳推理

多跳（multi-hop）在最近的顶会上关注度非常高，目前实现这一机制的方法也比较复杂。

\[EMNLP 2019\] What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

\[EMNLP 2019\] Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning

\[EMNLP 2019\] Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA

\[ACL 2019\] Multi-Hop Paragraph Retrieval for Open-Domain Question Answering

\[ACL 2019\] Dynamically Fused Graph Network for Multi-hop Reasoning

\[ACL 2019\] Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

\[ACL 2019\] Multi-hop Reading Comprehension through Question Decomposition and Rescoring

\[ACL 2019\] Compositional Questions Do Not Necessitate Multi-hop Reasoning

\[ACL 2019\] Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction

\[ACL 2019\] Cognitive Graph for Multi-Hop Reading Comprehension at Scale

\[ACL 2019\] Understanding Dataset Design Choices for Multi-hop Reasoning

\[NAACL 2019\] Repurposing Entailment for Multi-Hop Question Answering Tasks

\[NAACL 2019\] BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering

\[ACL 2019\] Exploiting Explicit Paths for Multi-hop Reading Comprehension

\[ACL 2019\] Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs

多语言/跨语言的问答

包括英法德等主流语言之间的研究，也包括特定于使用人数较少的语言的研究。

\[EMNLP 2019\] Cross-Lingual Machine Reading Comprehension

\[EMNLP 2019\] BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension Novels

\[ACL 2019\] XQA: A Cross-lingual Open-domain Question Answering Dataset

知识库问答和基于文本的问答的结合

前者通常是限定域的，知识容量有限，结构化信息比较好查询；后者通常是开放域的，信息量很大，但是提取知识比较困难。

\[EMNLP 2019\] Language Models as Knowledge Bases? 探索语言模型作为知识来源的可能性  
\[ACL 2019\] Interpretable Question Answering on Knowledge Bases and Text

\[ACL 2019\] Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

\[EMNLP 2019\] Incorporating External Knowledge into Machine Reading for Generative Question Answering

长文本/多段落

MRC 的研究在向多段落/长文本扩展。

\[EMNLP 2019\] BookQA: Stories of Challenges and Opportunities

\[ACL 2019\] Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives

\[ACL 2019\] ELI5: Long Form Question Answering

\[ACL 2018\] Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification

\[ACL 2019\] Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension

\[EMNLP 2019\] Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering

\[ACL 2019\] Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension

\[ACL 2019\] Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs

\[EMNLP19\] PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

QA 系统的可解释性

比如可以将对答案的解释也作为训练数据的一部分，让模型学会解释。

\[NAACL 2019\] Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering

\[EMNLP 2017\] QUINT: Interpretable Question Answering over Knowledge Bases

\[ACL 2019\] Interpretable Question Answering on Knowledge Bases and Text

\[ACL 2019\] Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

不可回答的问题

这个问题包括无法回答的问题和合理答案的判别两个任务。

\[AAAI 2019\] Read + Verify: Machine Reading Comprehension with Unanswerable Questions

\[ACL 2019\] Learning to Ask Unanswerable Questions for Machine Reading Comprehension

数据集的构建

更实用、智能、强大的 QA 系统需要更多优质的数据集来推动。

\[EMNLP 2019\] BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels 多语言与跨语言的小说阅读理解

\[EMNLP 2019\] GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level 高中地理场景下的问答基准测试

\[EMNLP 2019\] Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning 共指解析问题

\[IJCAI 2019\] AmazonQA: A Review-Based Question Answering Task 基于评论的问答

\[EMNLP 2019\] BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension Novels 多语言和跨语言阅读理解小说的双语并行数据集

\[ACL 2019\] XQA: A Cross-lingual Open-domain Question Answering Dataset 跨语言开放域问答数据集

\[ACL 2019\] WEETQA: A Social Media Focused Question Answering Dataset 社交媒体问答数据集

\[EMNLP 2019\] A Span-Extraction Dataset for Chinese Machine Reading Comprehension 中文阅读跨度提取数据集

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参考资料：  
[智能问答（Question Answering）的主要研究方向有哪些？][Question Answering]  
[Question Answering论文(问答系统&阅读理解)][Question Answering 1]  
[QA问答系统(Question Answering)][QA_Question Answering]  
[(含源码)「自然语言处理(NLP)」Question Answering(QA)论文整理(一)][NLP_Question Answering_QA]  
[揭开知识库问答KB-QA的面纱1·简介篇][KB-QA_1]  
[美团知识图谱问答技术实践与探索][Link 1]

[Question Answering]: https://blog.csdn.net/hecongqing/article/details/104586048
[Question Answering 1]: https://blog.csdn.net/orangerfun/article/details/107531356
[QA_Question Answering]: https://zhuanlan.zhihu.com/p/61914325
[NLP_Question Answering_QA]: https://mp.weixin.qq.com/s?__biz=MzUzOTgwNDMzOQ==&mid=2247484450&idx=1&sn=d82969f4590b93515f70b670d20572b1&chksm=fac393fecdb41ae8f1a865d0f96f5ebc34e89adab86eb50e2b2cea921d41e615602d68df6de2&scene=21#wechat_redirect
[KB-QA_1]: https://zhuanlan.zhihu.com/p/25735572
[Link 1]: https://mp.weixin.qq.com/s?__biz=MjM5NjQ5MTI5OA==&mid=2651765284&idx=1&sn=7801e682dd542424984d74d6ad09eed6&chksm=bd1263698a65ea7f931b6f7ce320d151323133be0bae37a3a4b27cc2eb53cd398e6828a4964d&scene=132#wechat_redirect