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.
Referring expression recognition and generation is a well studied problem in intelligent user interfaces @cite_21 , human-robot interaction @cite_15 @cite_19 @cite_0 , and situated dialogue @cite_17 . and introduce two benchmark datasets for referring expression recognition. Several models that leverage linguistic structure have been proposed. propose a model where the target and supporting objects (i.e. objects that are mentioned in order to disambiguate the target object) are identified and scored jointly. The resulting model is able to localize supporting objects without direct supervision. introduce a compositional approach for the RER task. They assume that the referring expression can be decomposed into a triplet consisting of the target object, the supporting object, and their spatial relationship. This structured model achieves state-of-the-art accuracy on the Google-Ref dataset. propose a type of neural modular network @cite_27 where the computation graph is defined in terms of a constituency parse of the input referring expression.
@cite_21 @cite_0 @cite_19 @cite_27 @cite_15 @cite_17
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Multimodal user interfaces allow users to interact with computers through multiple modalities, such as speech, gesture, and gaze. To be effective, multimodal user interfaces must correctly identify all objects which users refer to in their inputs. To systematically resolve different types of references, we have developed a probabilistic approach that uses a graph-matching algorithm. Our approach identifies the most probable referents by optimizing the satisfaction of semantic, temporal, and contextual constraints simultaneously. Our preliminary user study results indicate that our approach can successfully resolve a wide variety of referring expressions, ranging from simple to complex and from precise to ambiguous ones. In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world. Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning *neural module networks*, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes. In language-based interaction between a human and an artificial agent (e.g., robot) in a physical world, because the human and the agent have different knowledge and capabilities in perceiving the shared environment, referential grounding is very difficult. To facilitate such interaction, it is important for the agent to continuously learn and acquire knowledge about the environment through interactions with humans and incorporate the learned knowledge in grounding references from human utterances. To address this issue, this paper presents a graph-based approach for referential grounding and examines how referential grounding and word acquisition influence each other in physical world interaction. Our empirical results have shown that for most words, automated word acquisition through interaction improves referential grounding performance. However, this is not the case for words describing object types, where human supervision is important. Nevertheless, better referential grounding enables more accurate acquisition of word meanings, which in turn further improves grounding performance for references in subsequent utterances. A robot participating in natural dialogue with a human interlocutor may need to discuss, reason about, or initiate actions concerning dialogue-referenced entities. To do so, the robot must first identify or create new representations for those entities, a capability known as reference resolution. We previously presented algorithms for resolving references occurring in definite noun phrases. In this paper we present GH-POWER : an algorithm for resolving references occurring in a wider array of linguistic forms, by making novel extensions to the Givenness Hierarchy, and evaluate GH-POWER on natural task-based human-human and human-robot dialogues.