태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image-guided Story Ending Generation (IgSEG) aims to generate a story ending for a given multi-sentence story plot and an ending-related image.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
VIST-E consists of 49,913 training samples, 4,963 validation samples and 5,030 test samples, which is modified from VIST dataset. As every sample in VIST contains a story of five sentences, each sample in VIST-E contains the story ending, the ending-related image and the first four sentences in the story as the story context. Additionally, each sentence is trimmed down to a maximum of 40 words.
LSMDC-E contains 20,151 training samples, 1,477 validation samples and 2,005 test samples, which is modified from LSMDC 2021. We take the first four sentences in every five-sentence story as the story context and the last sentence as the story ending. As every sentence relates to a movie frame set in LSMDC, we take the last frame set as the ending-related image set for IgSEG.
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