TutorialVQA: Question Answering Dataset for Tutorial Videos
TutorialVQA dataset for instructional video QA, including 6,000 manually collected triples.
Key Findings
Methodology
The study introduces a new QA task focused on instructional videos. The methodology involves manually collecting triples of videos, questions, and answer spans. Video transcripts serve as the primary data source, with several baseline algorithms evaluated experimentally.
Key Results
- Result 1: Sentence prediction accuracy using RaSor model is only 14%, indicating task difficulty.
- Result 2: Segment retrieval accuracy using attentive LSTM model is 23%.
- Result 3: Pipeline method using TF-IDF embeddings achieves 63.85% accuracy in top 10 videos.
Significance
This research fills the gap in existing video QA datasets for multi-step and non-factoid answers, advancing the application of video transcripts in QA tasks. It holds significant implications for academia and industry, particularly in automated instructional video analysis.
Technical Contribution
Technical contributions include the introduction of a new dataset and task framework using video transcripts for QA. Compared to existing methods, it provides more complex answer formats and longer video segments as answers.
Novelty
This is the first QA task using video segments as answers in instructional videos. Unlike previous short text answer generation, this approach emphasizes video content localization and detailed step identification.
Limitations
- Limitation 1: The model's ability to locate answer segments in long videos is limited, leading to low accuracy.
- Limitation 2: Only video transcripts are used, without integrating multimodal information.
Future Work
Future work includes exploring the integration of multimodal information, developing more sophisticated models to enhance answer localization accuracy, and expanding the dataset to cover more domains.
AI Executive Summary
Despite the abundance of existing video QA datasets, most focus on simple factoid questions, overlooking the need for multi-step and non-factoid answers. To address this, researchers propose the TutorialVQA dataset, focusing on QA tasks in instructional videos. This dataset includes approximately 6,000 manually collected triples, covering videos, questions, and answer segments.
Researchers use video transcripts as the primary data source and evaluate through various baseline algorithms. Results show that existing models perform poorly on this task, particularly in locating answer segments in long videos. Sentence prediction accuracy using the RaSor model is only 14%, while segment retrieval accuracy using the attentive LSTM model is 23%.
This study not only fills the gap in existing datasets but also advances the application of video transcripts in QA tasks. Future work will explore the integration of multimodal information, develop more sophisticated models to enhance answer localization accuracy, and expand the dataset to cover more domains.
Deep Analysis
Background
Video as a primary medium for information delivery has been widely applied in QA tasks. However, existing datasets mostly focus on short videos and simple factoid questions, neglecting complex issues in long videos. Instructional videos, with their detailed steps and rich content, are ideal subjects for research.
Core Problem
The core problem is how to locate answers to multi-step and non-factoid questions in long videos. This requires identifying key segments in the video and providing detailed step information, rather than just simple short text answers.
Innovation
The innovations of this study include: 1) Proposing a new QA task focused on instructional videos; 2) Using video transcripts as the primary data source; 3) Providing more complex answer formats, covering multi-step and detailed operational instructions.
Methodology
- �� Dataset Collection: Manually collect triples of videos, questions, and answer segments.
- �� Baseline Models: Evaluate using RaSor and attentive LSTM.
- �� Data Processing: Use video transcripts for text analysis.
Experiments
The experimental design includes sentence prediction and segment retrieval using RaSor and attentive LSTM models. The dataset comprises 76 videos and 6,195 QA pairs. Evaluation metrics include accuracy and MRR.
Results
Results indicate poor performance of existing models on this task. Sentence prediction accuracy using RaSor is only 14%, while segment retrieval accuracy using attentive LSTM is 23%. The pipeline method using TF-IDF embeddings achieves 63.85% accuracy in top 10 videos.
Applications
The dataset can be used to develop more complex video QA systems, particularly in education and training fields. It offers new possibilities for automated instructional video analysis.
Limitations & Outlook
Limitations include the model's limited ability to locate answer segments in long videos, and the use of only video transcripts without integrating multimodal information. Future work will explore the integration of multimodal information and develop more sophisticated models.
Plain Language Accessible to non-experts
Imagine you're in a kitchen trying to cook a complex dish. You have a recipe book, but each step is very detailed. Now, you need an assistant to help you find the exact location of each step in the recipe. This assistant is like the model in our study. It analyzes every sentence in the video to help you find the correct steps. Although the assistant isn't perfect yet and sometimes finds the wrong steps, it's constantly learning and improving. In the future, it might combine more information, like images from the video, to help you complete the dish better.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game, and you need to find every hidden quest in the game. Our research is like a super helper, helping you find the exact location of all hidden quests in the game videos. Although this helper sometimes gets it wrong, it's constantly learning and getting smarter. In the future, it might combine game images to help you find all the quests faster! Isn't that cool?
Glossary
RaSor Model
A model for text QA by selecting start and end positions in the text to locate answers.
Used for sentence-level answer prediction.
Attentive LSTM
A long short-term memory network with attention mechanism for modeling text sequences.
Used for segment retrieval tasks.
TF-IDF
A text representation method measuring word importance by term frequency and inverse document frequency.
Used for text embeddings of videos and questions.
Video Transcript
The process of converting spoken content in a video to text.
Used as the primary data source for text analysis.
Multimodal Information
Information combining multiple data types, such as text, images, and audio.
Planned to be integrated in future work.
Open Questions Unanswered questions from this research
- 1 How to improve model accuracy in locating answer segments in long videos? Current methods perform poorly with long texts, requiring more sophisticated models.
- 2 How to integrate multimodal information to enhance QA system performance? Currently, only text information is used, without fully utilizing images and audio from videos.
Applications
Immediate Applications
Educational Video Analysis
Can be used for automated analysis of key steps in educational videos, helping students better understand complex concepts.
Long-term Vision
Multimodal QA Systems
Combine video, audio, and text information to develop smarter QA systems, enhancing user experience.
Abstract
Despite the number of currently available datasets on video question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, We propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000manually collected triples of (video, question, answer span). We also provide experimental results with several baselines algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.