Abstract

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We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.

Denotation Graph

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HTML view of the Denotation Graph
Denotation Graph Download

We define ⟦ s ⟧, the visual denotation of a linguistic expression s (e.g. a sentence, verb phrase or noun phrase), to be the set of images that depict what it describes. The denotation graph pairs a large number of linguistic expressions with their visual denotations, and defines a large subsumption hierarchy over these expressions. Consider the following fragment of our denotation graph¹:

Each node in the graph corresponds to a string s and its denotation: If s (e.g. s="two dogs running" ) is the parent of t (e.g. t="two dogs running in the grass" ) in the denotation graph, s is more generic than t, and there is one linguistic operation (here, the addition of the prepositional phrase "in the grass") that turns s into t. Hence, any image that depicts t (two dogs running in the grass) must also depict s (two dogs running), and ⟦ t ⟧ ⊆ ⟦ s ⟧. Thus, the visual denotation of a parent s will subsume the visual denotation of any of its children t.

We provide below the data files that make up the denotation graph we have created from the Flickr 30k Dataset. The graph consists of a set of strings that define the nodes of the graph (dog, running, grass, etc...), the edges that connect those nodes (dog running can be created from running by adding the subject dog, and can be created from dog by adding the verb running), and the images that depict each string in the graph.

Additionally, we have computed two different kinds of denotational similarity metrics computed on the nodes in the graph: (normalized) pointwise mutual information, PMI(s, t), and conditional probabilities, P(s | t ). Our paper shows that these similarity metrics are at least as beneficial as distributional similarities for two tasks that require semantic inference. For example:

s	PMI(s, play baseball)	P(play baseball | s)
tag him	0.673	0.600
hold bat	0.627	0.368
try to tag	0.616	0.517
slide into base	0.569	0.278
hold bat	0.627	0.368
pitch	0.561	0.200

Approximate Textual Entailment

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The approximate textual entailment task generates textual entailment items using the Flickr 30k Dataset and our denotation graph. We use captions from the Flickr 30k Dataset as premises, and try to determine if they entail strings from the denotation graph.

Premises:	A woman with dark hair in bending, open mouthed, towards the back of a dark headed toddler's head.
	A dark-haired woman has her mouth open and is hugging a little girl while sitting on a red blanket.
	A grown lady is snuggling on the couch with a young girl and the lady has a frightened look.
	A mom holding her child on a red sofa while they are both having fun.
Hypothesis:	make face