6,839 research outputs found
A Dataset for Movie Description
Descriptive video service (DVS) provides linguistic descriptions of movies
and allows visually impaired people to follow a movie along with their peers.
Such descriptions are by design mainly visual and thus naturally form an
interesting data source for computer vision and computational linguistics. In
this work we propose a novel dataset which contains transcribed DVS, which is
temporally aligned to full length HD movies. In addition we also collected the
aligned movie scripts which have been used in prior work and compare the two
different sources of descriptions. In total the Movie Description dataset
contains a parallel corpus of over 54,000 sentences and video snippets from 72
HD movies. We characterize the dataset by benchmarking different approaches for
generating video descriptions. Comparing DVS to scripts, we find that DVS is
far more visual and describes precisely what is shown rather than what should
happen according to the scripts created prior to movie production
Movie Description
Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for this task, including
Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from
inherent drawbacks. Mainly, the Markov assumption implies that, given the
immediate past, future alignment decisions are independent of further history.
The separation between similarity computation and alignment decision also
prevents end-to-end training. In this paper, we propose an end-to-end neural
architecture where alignment actions are implemented as moving data between
stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture
supports a large variety of alignment tasks, including one-to-one, one-to-many,
skipping unmatched elements, and (with extensions) non-monotonic alignment.
Extensive experiments on semi-synthetic and real datasets show that our
algorithm outperforms state-of-the-art baselines.Comment: Accepted at CVPR 2018 (Spotlight). arXiv file includes the paper and
the supplemental materia
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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Adapting Automatic Summarization to New Sources of Information
English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media, and foreign-language news articles are accessible to, if not immediately understandable by, English-speaking users. This thesis focuses on developing automatic summarization techniques for these new sources of information.
We focus on summarizing two specific new sources of information: personal narratives, first-person accounts of exciting or unusual events that are readily found in blog entries and other social media posts, and non-English documents, which must first be translated into English, often introducing translation errors that complicate the summarization process. Personal narratives are a very new area of interest in natural language processing research, and they present two key challenges for summarization. First, unlike many news articles, whose lead sentences serve as summaries of the most important ideas in the articles, personal narratives provide no such shortcuts for determining where important information occurs in within them; second, personal narratives are written informally and colloquially, and unlike news articles, they are rarely edited, so they require heavier editing and rewriting during the summarization process. Non-English documents, whether news or narrative, present yet another source of difficulty on top of any challenges inherent to their genre: they must be translated into English, potentially introducing translation errors and disfluencies that must be identified and corrected during summarization.
The bulk of this thesis is dedicated to addressing the challenges of summarizing personal narratives found on the Web. We develop a two-stage summarization system for personal narrative that first extracts sentences containing important content and then rewrites those sentences into summary-appropriate forms. Our content extraction system is inspired by contextualist narrative theory, using changes in writing style throughout a narrative to detect sentences containing important information; it outperforms both graph-based and neural network approaches to sentence extraction for this genre. Our paraphrasing system rewrites the extracted sentences into shorter, standalone summary sentences, learning to mimic the paraphrasing choices of human summarizers more closely than can traditional lexicon- or translation-based paraphrasing approaches.
We conclude with a chapter dedicated to summarizing non-English documents written in low-resource languages – documents that would otherwise be unreadable for English-speaking users. We develop a cross-lingual summarization system that performs even heavier editing and rewriting than does our personal narrative paraphrasing system; we create and train on large amounts of synthetic errorful translations of foreign-language documents. Our approach produces fluent English summaries from disdisfluent translations of non-English documents, and it generalizes across languages
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