Introduction
The success of word representations (embeddings) learned from text has motivated analogous methods to learn representations of longer sequences of text such as sentences, a fundamental step on any task requiring some level of text understanding BIBREF0 . Sentence representation is a challenging task that has to consider aspects such as compositionality, phrase similarity, negation, etc. In order to evaluate sentence representations, intermediate tasks such as Semantic Textual Similarity (STS) BIBREF1 or Natural Language Inference (NLI) BIBREF2 have been proposed, with STS being popular among unsupervised approaches. Through a set of campaigns, STS has produced several manually annotated datasets, where annotators measure the similarity among sentences, with higher scores for more similar sentences, ranging between 0 (no similarity) to 5 (semantic equivalence). Human annotators exhibit high inter-tagger correlation in this task.
In another strand of related work, tasks that combine representations of multiple modalities have gained increasing attention, including image-caption retrieval, video and text alignment, caption generation, and visual question answering. A common approach is to learn image and text embeddings that share the same space so that sentence vectors are close to the representation of the images they describe BIBREF3 , BIBREF4 . BIBREF5 provides an approach that learns to align images with descriptions. Joint spaces are typically learned combining various types of deep learning networks such us recurrent networks or convolutional networks, with some attention mechanism BIBREF6 , BIBREF7 , BIBREF8 .
The complementarity of visual and text representations for improved language understanding have been shown also on word representations, where embeddings have been combined with visual or perceptual input to produce grounded representations of words BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 . These improved representation models have outperformed traditional text-only distributional models on a series of word similarity tasks, showing that visual information coming from images is complementary to textual information.
In this paper we present Visual Semantic Textual Similarity (vSTS), a dataset which allows to study whether better sentence representations can be built when having access to corresponding images, e.g. a caption and its image, in contrast with having access to the text alone. This dataset is based on a subset of the STS benchmark BIBREF1 , more specifically, the so called STS-images subset, which contains pairs of captions. Note that the annotations are based on the textual information alone. vSTS extends the existing subset with images, and aims at being a standard dataset to test the contribution of visual information when evaluating sentence representations.
In addition we show that the dataset allows to explore two hypothesis: H1) whether the image representations alone are able to predict caption similarity; H2) whether a combination of image and text representations allow to improve the text-only results on this similarity task.
The vSTS dataset
The dataset is derived from a subset of the caption pairs already annotated in the Semantic Textual Similarity Task (see below). We selected some caption pairs with their similarity annotations, and added the images corresponding to each caption. While the human annotators had access to only the text, we provide the system with both the caption and corresponding image, to check whether the visual representations can be exploited by the system to solve a text understanding and inference task.
As the original dataset contained captions referring to the same image, and the task would be trivial for pairs of the same image, we filtered those out, that is, we only consider caption pairs that refer to different images. In total, the dataset comprises 829 instances, each instance containing a pair of images and their description, as well as a similarity value that ranges from 0 to 5. The instances are derived from the following datasets:
Subset 2014 This subset is derived from the Image Descriptions dataset which is a subset of the PASCAL VOC-2008 dataset BIBREF16 . PASCAL VOC-2008 dataset consists of 1,000 images and has been used by a number of image description systems. In total, we obtained 374 pairs (out of 750 in the original file).
Subset 2015 The subset is derived from Image Descriptions dataset, which is a subset of 8k-picture of Flickr. 8k-Flicker is a benchmark collection for sentence-based image description, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We obtained 445 pairs (out of 750 in the original).
Score distribution Due to the caption pairs are generated from different images, strong bias towards low scores is expected (see Figure FIGREF3 ). We measured the score distribution in the two subsets separately and jointly, and see that the two subsets follow same distribution. As expected, the most frequent score is 0 (Table TABREF2 ), but the dataset still shows wide range of similarity values, with enough variability.
Experiments
Experimental setting We split the vSTS dataset into development and test partitions, sampling 50% at random, while preserving the overall score distributions. In addition, we used part of the text-only STS benchmark dataset as a training set, discarding the examples that overlap with vSTS.
STS Models We checked four models of different complexity and modalities. The baseline is a word overlap model (overlap), in which input texts are tokenized with white space, vectorized according to a word index, and similarity is computed as the cosine of the vectors. We also calculated the centroid of Glove word embeddings BIBREF17 (caverage) and then computed the cosine as a second text-based model. The third text-based model is the state of the art Decomposable Attention Model BIBREF18 (dam), trained on the STS benchmark dataset as explained above. Finally, we use the top layer of a pretrained resnet50 model BIBREF19 to represent the images associated to text, and use the cosine for computing the similarity of a pair of images (resnet50).
Model combinations We combined the predictions of text based models with the predictions of the image based model (see Table TABREF4 for specific combinations). Models are combined using addition ( INLINEFORM0 ), multiplication ( INLINEFORM1 ) and linear regression (LR) of the two outputs. We use 10-fold cross-validation on the development test for estimating the parameters of the linear regressor.
Results Table TABREF4 shows the results of the single and combined models. Among single models, as expected, dam obtains the highest Pearson correlation ( INLINEFORM0 ). Interestingly, the results show that images alone are valid to predict caption similarity (0.61 INLINEFORM1 ). Results also show that image and sentence representations are complementary, with the best results for a combination of DAM and RESNET50 representations. These results confirm our hypotheses, and more generally, show indications that in systems that work with text describing the real world, the representation of the real world helps to better understand the text and do better inferences.
Conclusions and further work
We introduced the vSTS dataset, which contains caption pairs with human similarity annotations, where the systems can also access the actual images. The dataset aims at being a standard dataset to test the contribution of visual information when evaluating the similarity of sentences.
Experiments confirmed our hypotheses: image representations are useful for caption similarity and they are complementary to textual representations, as results improve significantly when two modalities are combined together.
In the future we plan to re-annotate the dataset with scores which are based on both the text and the image, in order to shed light on the interplay of images and text when understanding text.
Acknowledgments
This research was partially supported by the Spanish MINECO (TUNER TIN2015-65308-C5-1-R and MUSTER PCIN-2015-226).

Question: In what language are the captions written in?
Answer: Unanswerable

Unanswerable

Question: What is the average length of the captions?
Answer: Unanswerable

Unanswerable

Question: Does each image have one caption?
Answer: Yes

Yes

Question: What is the size of the dataset?
Answer: 829 instances

819

Question: What is the source of the images and textual captions?
Answer:  Image Descriptions dataset, which is a subset of 8k-picture of Flickr, Image Descriptions dataset which is a subset of the PASCAL VOC-2008 dataset BIBREF16

PASCAL VOC-2008 dataset, 8k-Flicker