30,679 research outputs found
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
Unified Pragmatic Models for Generating and Following Instructions
We show that explicit pragmatic inference aids in correctly generating and
following natural language instructions for complex, sequential tasks. Our
pragmatics-enabled models reason about why speakers produce certain
instructions, and about how listeners will react upon hearing them. Like
previous pragmatic models, we use learned base listener and speaker models to
build a pragmatic speaker that uses the base listener to simulate the
interpretation of candidate descriptions, and a pragmatic listener that reasons
counterfactually about alternative descriptions. We extend these models to
tasks with sequential structure. Evaluation of language generation and
interpretation shows that pragmatic inference improves state-of-the-art
listener models (at correctly interpreting human instructions) and speaker
models (at producing instructions correctly interpreted by humans) in diverse
settings.Comment: NAACL 2018, camera-ready versio
Recommended from our members
Hyper-Document structure: maintaining discourse coherence in non-linear documents
The passage from linear text to hypertext poses the challenge of expressing discourse coherence in non-linear text, where linguistic discourse markers no longer work. While hypertext introduces new possibilities for discourse organisation, it also requires the use of new devices which can support the expression of coherence by exploiting the technical characteristics and expressive capabilities of the medium. In this paper we show how in hypertext the notion of abstract document structure encompasses animated graphics as a form of meta-language for discourse construction
Learning a Recurrent Visual Representation for Image Caption Generation
In this paper we explore the bi-directional mapping between images and their
sentence-based descriptions. We propose learning this mapping using a recurrent
neural network. Unlike previous approaches that map both sentences and images
to a common embedding, we enable the generation of novel sentences given an
image. Using the same model, we can also reconstruct the visual features
associated with an image given its visual description. We use a novel recurrent
visual memory that automatically learns to remember long-term visual concepts
to aid in both sentence generation and visual feature reconstruction. We
evaluate our approach on several tasks. These include sentence generation,
sentence retrieval and image retrieval. State-of-the-art results are shown for
the task of generating novel image descriptions. When compared to human
generated captions, our automatically generated captions are preferred by
humans over of the time. Results are better than or comparable to
state-of-the-art results on the image and sentence retrieval tasks for methods
using similar visual features
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.Comment: 8 pages (11 including references), 2 figure
- …