522,176 research outputs found
What is the role of recurrent neural networks (RNNs) in an image caption generator?
In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be `injected' into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can instead be viewed as only encoding the previously generated words. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. This paper compares these two architectures. We find that, in general, late merging outperforms injection, suggesting that RNNs are better viewed as encoders, rather than generators.peer-reviewe
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.Comment: The 9th International Natural Language Generation conference INLG,
2016. 10 pages, 2 figures, 3 table
Presuppositions in Context: Constructing Bridges
About the book: The First International and Interdisciplinary Conference on Modelling and Using Context, Rio de Janeiro, January 1997, gave rise to the present book, which contains a selection of the papers presented there, thoroughly refereed and revised. The treatment of contexts as bona fide objects of logical formalisation has gained wide acceptance, following the seminal impetus given by McCarthy in his Turing Award address. The field of natural language offers a particularly rich variety of examples and challenges to researchers concerned with the formal modelling of context, and several chapters in the volume deal with contextualisation in the setting of natural language. Others adopt a purely formal-logical viewpoint, seeking to develop general models of even wider applicability. The 12 chapters are organised in three groups: formalisation of contextual information in natural language understanding and generation, the application of context in mechanised reasoning domains, and novel non-classical logics for contextual application
Evaluating NLG systems: A brief introduction
This year the International Conference on Natural Language Generation (INLG)
will feature an award for the paper with the best evaluation. The purpose of
this award is to provide an incentive for NLG researchers to pay more attention
to the way they assess the output of their systems. This essay provides a short
introduction to evaluation in NLG, explaining key terms and distinctions.Comment: To be published on the INLG2023 conference websit
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
In neural image captioning systems, a recurrent neural network (RNN) is
typically viewed as the primary `generation' component. This view suggests that
the image features should be `injected' into the RNN. This is in fact the
dominant view in the literature. Alternatively, the RNN can instead be viewed
as only encoding the previously generated words. This view suggests that the
RNN should only be used to encode linguistic features and that only the final
representation should be `merged' with the image features at a later stage.
This paper compares these two architectures. We find that, in general, late
merging outperforms injection, suggesting that RNNs are better viewed as
encoders, rather than generators.Comment: Appears in: Proceedings of the 10th International Conference on
Natural Language Generation (INLG'17
Quantum Natural Language Generation on Near-Term Devices
The emergence of noisy medium-scale quantum devices has led to
proof-of-concept applications for quantum computing in various domains.
Examples include Natural Language Processing (NLP) where sentence
classification experiments have been carried out, as well as procedural
generation, where tasks such as geopolitical map creation, and image
manipulation have been performed. We explore applications at the intersection
of these two areas by designing a hybrid quantum-classical algorithm for
sentence generation.
Our algorithm is based on the well-known simulated annealing technique for
combinatorial optimisation. An implementation is provided and used to
demonstrate successful sentence generation on both simulated and real quantum
hardware. A variant of our algorithm can also be used for music generation.
This paper aims to be self-contained, introducing all the necessary
background on NLP and quantum computing along the way.Comment: To appear in proceedings of International Natural Language Generation
Conference (INLG) 202
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