Chinese Movie Dialogue Question Answering Dataset
This paper constructs a Chinese dialogue-based information-seeking question answering dataset CMDQA, which is mainly applied to the scenario of getting Chinese movie related information. It contains 10K QA dialogs (40K turns in total). All questions and background documents are compiled from the Wikipedia via an Internet crawler. The answers to the questions are obtained via extracting the corresponding answer spans within the related text passage. In CMDQA, in addition to searching related documents, pronouns are also added to the question to better mimic the real dialog scenario. This dataset can test the individual performance of the information retrieval, the question answering and the question re-writing modules. This paper also provides a baseline system and shows its performance on this dataset. The experiments elucidate that it still has a big gap to catch the human performance. This dataset thus provides enough challenge for the researcher to conduct related research.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalQuestion AnsweringRetrievalSimilar Papers 제목 키워드 기반
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions
To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen. Inspired by this behaviour, we design ROLL, a model for knowle…
Question AnsweringVideo Question AnsweringVisual Question Answering (VQA)Representing Movie Characters in Dialogues
We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other pa…
Question AnsweringRelation ClassificationVisual Question AnsweringVisual Question Answering (VQA)MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark
Understanding real-world videos such as movies requires integrating visual and dialogue cues. Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and, given the difficulty of evaluating free-for…
Multimodal ReasoningVisual ReasoningMedChatZH: a Better Medical Adviser Learns from Better Instructions
Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these…
Question AnsweringLearning Video Context as Interleaved Multimodal Sequences
Narrative videos, such as movies, pose significant challenges in video understanding due to their rich contexts (characters, dialogues, storylines) and diverse demands (identify who, relationship, and reason). In this pa…
Language ModelingLanguage ModellingQuestion AnsweringText Retrieval+5