74,723 research outputs found
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Understanding language goes hand in hand with the ability to integrate
complex contextual information obtained via perception. In this work, we
present a novel task for grounded language understanding: disambiguating a
sentence given a visual scene which depicts one of the possible interpretations
of that sentence. To this end, we introduce a new multimodal corpus containing
ambiguous sentences, representing a wide range of syntactic, semantic and
discourse ambiguities, coupled with videos that visualize the different
interpretations for each sentence. We address this task by extending a vision
model which determines if a sentence is depicted by a video. We demonstrate how
such a model can be adjusted to recognize different interpretations of the same
underlying sentence, allowing to disambiguate sentences in a unified fashion
across the different ambiguity types.Comment: EMNLP 201
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Clue: Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to
study the information needs and goals of image captioning. Using an annotation
protocol specifically devised for capturing image--caption coherence relations,
we annotate 10,000 instances from publicly-available image--caption pairs. We
introduce a new task for learning inferences in imagery and text, coherence
relation prediction, and show that these coherence annotations can be exploited
to learn relation classifiers as an intermediary step, and also train
coherence-aware, controllable image captioning models. The results show a
dramatic improvement in the consistency and quality of the generated captions
with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202
A Survey of Current Datasets for Vision and Language Research
Integrating vision and language has long been a dream in work on artificial
intelligence (AI). In the past two years, we have witnessed an explosion of
work that brings together vision and language from images to videos and beyond.
The available corpora have played a crucial role in advancing this area of
research. In this paper, we propose a set of quality metrics for evaluating and
analyzing the vision & language datasets and categorize them accordingly. Our
analyses show that the most recent datasets have been using more complex
language and more abstract concepts, however, there are different strengths and
weaknesses in each.Comment: To appear in EMNLP 2015, short proceedings. Dataset analysis and
discussion expanded, including an initial examination into reporting bias for
one of them. F.F. and N.M. contributed equally to this wor
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