BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts (2022.lrec-1)
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| Challenge: | Question-Answer (QA) is an effective method for storing knowledge . prior QA identification systems have been limited to formal written documents . a large-scale QA dataset annotated by human over 500 hours of video transcripts is a challenge . |
| Approach: | They present a large-scale QA identification dataset annotated by human over 500 hours of video transcripts. |
| Outcome: | The proposed dataset presents unique challenges for existing methods . it shows that the annotated dataset presents challenges for new methods - the results will be released . |
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Building Question-Answer Data Using Web Register Identification (2024.lrec-main)
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| Challenge: | Recent advances in web register (genre) identification have created a shortage of QA datasets for English and Finnish. |
| Approach: | They propose a machine learning-based method for extracting QA pairs from web-scale data using XLM-R and a multilingual CORE web register corpus . they then develop a NER-style token classifier to identify the QA text spans within these documents. |
| Outcome: | The proposed method is adaptable to any language given the availability of language models and extensive web data, but it is limited to English and Finnish. |
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript (2022.findings-naacl)
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| Challenge: | a growing number of livestreaming videos provide useful knowledge with exceptional visual demonstrations. |
| Approach: | They propose a human-annotated corpus for punctuation restoration in livestreaming video transcripts . they show popular natural language processing tools underperform on sentence boundary detection . |
| Outcome: | The proposed dataset shows that natural language processing tools underperform on sentence boundary detection on livestreaming video transcripts. |
LifeQA: A Real-life Dataset for Video Question Answering (2020.lrec-1)
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Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
| Challenge: | Existing video question answering datasets consist of movies and TV shows, but they are not representative of our day-to-day lives. |
| Approach: | They propose a benchmark dataset for video question answering that focuses on day-to-day situations. |
| Outcome: | The proposed dataset analyzes the challenging but realistic aspects of LifeQA . it consists of video clips and over 2.3k multiple-choice questions . |
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts (2022.lrec-1)
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| Challenge: | livestreaming videos contain a considerable amount of off-topic content, causing noises and data load to downstream applications. |
| Approach: | They propose a human-annotated benchmark dataset for off-topic detection in livestreaming video transcripts. |
| Outcome: | The proposed dataset reveals the complexity of chitchat detection in livestreaming videos . livestreams tend to be longer than pre-recorded videos and have fewer verbal pauses . |
Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
| Outcome: | The proposed approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. |
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |
TVQA: Localized, Compositional Video Question Answering (D18-1)
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| Challenge: | Recent studies have focused on image-based question-answering (QA) tasks, but little has been done on video-based QA. |
| Approach: | They present a large-scale video QA dataset based on 6 popular TV shows . they provide analysis of the new dataset and trainable neural network framework . |
| Outcome: | The proposed dataset includes 152,545 QA pairs from 21,793 clips spanning over 460 hours of video. |
PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations (2023.findings-acl)
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| Challenge: | Mars? - PragmatiCQA |
| Approach: | Mars? - The Paper . |
| Outcome: | The proposed dataset features 6873 QA pairs that explores pragmatic reasoning in conversations over a diverse set of topics. |
emrQA: A Large Corpus for Question Answering on Electronic Medical Records (D18-1)
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| Challenge: | Existing annotations for other NLP tasks are used to generate domain-specific large-scale question answering (QA) datasets. |
| Approach: | They propose to re-purpose existing annotations for other NLP tasks by generating a large-scale question answering corpus using 1 million questions-logical form and 400,000+ question-answer evidence pairs. |
| Outcome: | The proposed model can be trained to learn domain-specific large-scale question answering (QA) datasets. |
Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)
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| Challenge: | Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges . |
| Approach: | They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs. |
| Outcome: | The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable. |