We present an empirical analysis of the state-of-the-art systems for referring expression recognition -- the task of identifying the object in an image referred to by a natural language expression -- with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore the linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image @math can achieve a precision of 71.2 in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2 in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning -- and further, how our data is constructed -- is critical as we seek to make substantive progress on grounded language tasks.
Previous studies on other tasks have found that the state-of-the-art systems may be successful for reasons different than originally assumed. For example, show that a simple logistic regression baseline with carefully defined features can achieve competitive results for reading comprehension on CNN Daily Mail datasets @cite_2 , indicating that more sophisticated models may be learning relatively simple correlations. Similarly, reveal bias in a dataset for semantic inference by demonstrating a simple model that achieves competitive results .
@cite_2
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Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.