WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021.emnlp-main)
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| Challenge: | Using a web page and a question, a machine can't understand the contents of web pages. |
| Approach: | They propose a novel dataset for web-based structural reading comprehension that consists of 400K question-answer pairs and a dataset of 6.4K web pages. |
| Outcome: | The proposed dataset consists of 400K question-answer pairs, collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. |
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| Challenge: | Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases. |
| Approach: | They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing. |
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
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| Challenge: | a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web. |
| Approach: | This tutorial examines methods for extracting information from the World Wide Web . it explores the commonalities in the challenges and solutions developed to address these different forms of text . |
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WebDP: Understanding Discourse Structures in Semi-Structured Web Documents (2023.findings-acl)
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| Challenge: | Web documents are one of the most primary and biggest data resources in current era, and understanding their discourse structure will benefit various downstream document processing applications. |
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English Machine Reading Comprehension Datasets: A Survey (2021.emnlp-main)
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| Challenge: | a survey of English Machine Reading Comprehension datasets is carried out . the aim is to provide a concise yet informative overview of the landscape . |
| Approach: | They survey 60 English Machine Reading Comprehension datasets to provide a resource for other researchers interested in this problem. |
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Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
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A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
STARC: Structured Annotations for Reading Comprehension (2020.acl-main)
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| Challenge: | STARC is an annotation framework for reading comprehension with multiple choice questions . existing annotation frameworks have limited support for reading behavior analyses . |
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The Web as a Knowledge-Base for Answering Complex Questions (N18-1)
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| Challenge: | Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. |
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How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
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| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
| Outcome: | The proposed models perform on 14 out of 20 bAbI, SQuAD, CBT, CNN and Who-did-What datasets. |
MultiSpanQA: A Dataset for Multi-Span Question Answering (2022.naacl-main)
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| Challenge: | Existing reading comprehension datasets focus on single-span answers, but multi-spread questions are less studied. |
| Approach: | They propose a new reading comprehension dataset that focuses on multi-span questions . they introduce new metrics for the purposes of multi--spontaneous question answering evaluation . |
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