20,602 research outputs found
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
We propose a process for investigating the extent to which sentence
representations arising from neural machine translation (NMT) systems encode
distinct semantic phenomena. We use these representations as features to train
a natural language inference (NLI) classifier based on datasets recast from
existing semantic annotations. In applying this process to a representative NMT
system, we find its encoder appears most suited to supporting inferences at the
syntax-semantics interface, as compared to anaphora resolution requiring
world-knowledge. We conclude with a discussion on the merits and potential
deficiencies of the existing process, and how it may be improved and extended
as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page
An integrated theory of language production and comprehension
Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal
Guided Open Vocabulary Image Captioning with Constrained Beam Search
Existing image captioning models do not generalize well to out-of-domain
images containing novel scenes or objects. This limitation severely hinders the
use of these models in real world applications dealing with images in the wild.
We address this problem using a flexible approach that enables existing deep
captioning architectures to take advantage of image taggers at test time,
without re-training. Our method uses constrained beam search to force the
inclusion of selected tag words in the output, and fixed, pretrained word
embeddings to facilitate vocabulary expansion to previously unseen tag words.
Using this approach we achieve state of the art results for out-of-domain
captioning on MSCOCO (and improved results for in-domain captioning). Perhaps
surprisingly, our results significantly outperform approaches that incorporate
the same tag predictions into the learning algorithm. We also show that we can
significantly improve the quality of generated ImageNet captions by leveraging
ground-truth labels.Comment: EMNLP 201
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
Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]
Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing
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