15,432 research outputs found
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
Multi-Task Video Captioning with Video and Entailment Generation
Video captioning, the task of describing the content of a video, has seen
some promising improvements in recent years with sequence-to-sequence models,
but accurately learning the temporal and logical dynamics involved in the task
still remains a challenge, especially given the lack of sufficient annotated
data. We improve video captioning by sharing knowledge with two related
directed-generation tasks: a temporally-directed unsupervised video prediction
task to learn richer context-aware video encoder representations, and a
logically-directed language entailment generation task to learn better
video-entailed caption decoder representations. For this, we present a
many-to-many multi-task learning model that shares parameters across the
encoders and decoders of the three tasks. We achieve significant improvements
and the new state-of-the-art on several standard video captioning datasets
using diverse automatic and human evaluations. We also show mutual multi-task
improvements on the entailment generation task.Comment: ACL 2017 (14 pages w/ supplementary
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
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