HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering
By Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Lixin Su, Xueqi Cheng. 2019
38 citations recorded by the source corpus.
This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HASQA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Riedel
- Mohit Bansal
- Hannaneh Hajishirzi
- Jianfeng Gao
- Hinrich Schütze
- Fabio Petroni
- Luke S. Zettlemoyer
- Patrick Lewis
In Computer Vision and Pattern Recognition
- Sebastian Riedel
- Patrick Lewis
- Fabio Petroni
- Yuxing Peng
- Minghao Hu
- Aleksandra Piktus
- Xueqi Cheng
- Liang Pang
In Natural Language Processing
- Sebastian Riedel
- Mohit Bansal
- Fabio Petroni
- Hannaneh Hajishirzi
- Patrick Lewis
- Tim Rocktaschel
- Jianfeng Gao
- Yuxiang Wu
Citations in the corpus
- How Context Affects Language Models' Factual Predictions (2020, background): “…2017, Wang et al., 2018b,c, Lin et al., 2018, Min et al., 2018, Lee et al., 2018, 2019, Das et al., 2019, Xiong et al., 2019], improving answer aggregation [Clark and Gardner, 2018, Wang et al., 2018c, Lee et al., 2018, Pang et al., 2019], and accelerating the whole pipeline [Seo et al., 2019].”
- Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (2019, background): “Third , passage ranker for selecting high-quality passages has been shown to be very useful in previous open-domain QA systems (Wang et al., 2018a; Lin et al., 2018; Pang et al., 2019).”
- Adaptive Information Seeking for Open-Domain Question Answering (2021, methodology): “In the retrieval stage, most approaches employ a de-terminate retrieval function and treat each passage independently (Wang et al., 2018; Lin et al., 2018; Lee et al., 2018; Yang et al., 2018; Pang et al., 2019; Lee et al., 2019; Guu et al., 2020; Karpukhin et al., 2020; Izacard and Grave, 2021).”
- Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (2019, background): “There are some other works that handle this task in different perspectives, such as using hierarchical answer span representations (Pang et al., 2019), modeling the interaction between the retriever and the reader (Das et al.”
- Named Entity Recognition for Social Media Texts with Semantic Augmentation (2020, background): “Thus, named entity recognition (NER) is a critical step for detecting proper entities in texts and providing support for down-stream natural language processing (NLP) tasks (Pang et al., 2019; Martins et al., 2019).”