Collecting Highly Parallel Data for Paraphrase Evaluation

By David L. Chen, William B. Dolan. 2011

1,514 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, methodology): “To assess how the denotational similarities perform on a more established task and domain, we apply them to the 1500 sentence pairs from the MSR Video Description Corpus (Chen and Dolan, 2011) that were annotated for the SemEval 2012 Semantic Textual Similarity (STS) task (Agirre et al., 2012).”
Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks (2021, methodology): “Our pre-trained model is evaluated on MSVD [11] dataset.”
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval (2021, methodology): “MSVD [9] consists of 80K English descriptions for 1,970 videos from YouTube, with each video containing 40 sentences each.”
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research (2019, background): “Moreover, our V A T E X dataset contains both English and Chinese descriptions at scale, which is an order of magnitude larger than MSVD [14].”
GIT: A Generative Image-to-text Transformer for Vision and Language (2022, methodology): “9 (a) MSVD-QA [112, 9] (b) MSRVTT-QA [112, 114] (c) TGIF-Frame [47]”
Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, background): “Table 10. Action retrieval datasets. Dataset Segments Cap. Cap. Length Cap./Seg. Action Length Rel. Caps. Vid. Source Cap. Source MSR-VTT [7] 10,000 200,000 9.3 20.0 15.0s 20.0 YouTube Collected MSVD [97] 2,089 122,665 7.1 40.9 9.8s 40.9 YouTube Collected LSMDC [28] 69,000 68,000 9.6 1.0 3.9s 1.0 Fi”
Dense-Captioning Events in Videos (2017, background): “ [40] cooking 44 600s - - - - - ActivityNet [4] human 20k 180s - - - - - MPII MD [39] movie 68k 4s 68,375 X - - - M-VAD [47] movie 49k 6s 55,904 X - - - MSR-VTT [55] open 10k 20s 200,000 X - - - MSVD [6] human 2k 10s 70,028 X - - - YouCook [7] cooking 88 - 2,688 X - - - Charades [43] human 10k 30s 1”
A Joint Sequence Fusion Model for Video Question Answering and Retrieval (2018, background): “Recently, various video-language tasks have drawn a lot of interests in computer vision research [1,2,3], including video captioning [4,5,6,7,8,9], video question answering (QA) [10,11], and video retrieval for a natural language query [8,12,13].”
MovieQA: Understanding Stories in Movies through Question-Answering (2015, background): “Most available datasets focus on image [17, 22, 46, 50] or video description [5, 32, 9].”
PipeDream: Fast and Efficient Pipeline Parallel DNN Training (2018, methodology): “When training the S2VT [43] model on the MSVD [3] dataset, PipeDream speeds up training by 3x compared to data-parallel BSP.”
A dataset for Movie Description (2015, background): “While recently several large datasets have been released which provide images with descriptions [51, 29, 47], video description datasets focus on short video snippets only and are limited in size [12] or not publicly available [52].”
Sequence to Sequence -- Video to Text (2015, methodology): “We report results on three video description corpora, namely the Microsoft Video Description corpus (MSVD) [3], the MPII Movie Description Corpus (MPII-MD) [28], and the Montreal Video Annotation Dataset (M-VAD) [37].”
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding (2016, neutral): “Recent papers have also used movies to focus on the video description problem leading to several datasets such as MSVD[20], M-VAD [21], and MPII-MD [11].”
Translating Videos to Natural Language Using Deep Recurrent Neural Networks (2014, methodology): “We perform all our experiments on the Microsoft Research Video Description Corpus (Chen and Dolan, 2011).”
Localizing Moments in Video with Natural Language (2017, background): “In contrast to other dataset collection efforts [5], we allow annotations to reference the camera, and believe such annotations may be helpful for applications like text-assisted video editing.”
Describing Videos by Exploiting Temporal Structure (2015, methodology): “We evaluate the effectiveness of the proposed mechanisms for exploiting temporal structure on the most widely used open-domain video description dataset, called the Youtube2Text dataset [5], which consists of 1,970 video clips with multiple descriptions per video.”