30,145 research outputs found
Reasoning About Pragmatics with Neural Listeners and Speakers
We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Referring expressions are natural language constructions used to identify
particular objects within a scene. In this paper, we propose a unified
framework for the tasks of referring expression comprehension and generation.
Our model is composed of three modules: speaker, listener, and reinforcer. The
speaker generates referring expressions, the listener comprehends referring
expressions, and the reinforcer introduces a reward function to guide sampling
of more discriminative expressions. The listener-speaker modules are trained
jointly in an end-to-end learning framework, allowing the modules to be aware
of one another during learning while also benefiting from the discriminative
reinforcer's feedback. We demonstrate that this unified framework and training
achieves state-of-the-art results for both comprehension and generation on
three referring expression datasets. Project and demo page:
https://vision.cs.unc.edu/referComment: Some typo fixed; comprehension results on refcocog updated; more
human evaluation results adde
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
Text to 3D Scene Generation with Rich Lexical Grounding
The ability to map descriptions of scenes to 3D geometric representations has
many applications in areas such as art, education, and robotics. However, prior
work on the text to 3D scene generation task has used manually specified object
categories and language that identifies them. We introduce a dataset of 3D
scenes annotated with natural language descriptions and learn from this data
how to ground textual descriptions to physical objects. Our method successfully
grounds a variety of lexical terms to concrete referents, and we show
quantitatively that our method improves 3D scene generation over previous work
using purely rule-based methods. We evaluate the fidelity and plausibility of
3D scenes generated with our grounding approach through human judgments. To
ease evaluation on this task, we also introduce an automated metric that
strongly correlates with human judgments.Comment: 10 pages, 7 figures, 3 tables. To appear in ACL-IJCNLP 201
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