We extend the recent framework of Osokin et al.

We provide this code as a template to extend MRL to any domain.

These settings are used throughout our study. In Tab. 1 we show the measured FID scores between each For each dataset we show examples for an easy, medium and hard train-test split. Tab. 2 first illustrates the FID scores for all pairwise combinations However, the fact that FID scores are relatively close to another despite large semantic differences between datasets may indicate that FID based on our utilised FID estimator (Sec. This section provides additional results for the experiments presented in Sec. 4 in the main paper. To this end, we provide the exact performance values used to visualize Figure 1 in the main paper in Tab.

Most of the Natural Language Processing systems are involved in entity-based processing for several tasks like Information Extraction, Question-Answering, Text-Summarization and so on. A new challenge comes when entities play roles according to their act or attributes in certain context. Entity Role Detection is the task of assigning such roles to the entities. Usually real-world entities are of types: person, location and organization etc. Roles could be considered as domain-dependent subtypes of these types. In the cases, where retrieving a subset of entities based on their roles is needed, poses the problem of defining the role and entities having those roles. This paper presents the study of study of solving Entity Role Detection problem by modeling it as Named Entity Recognition (NER) and Entity Retrieval/Ranking task. In NER, these roles could be considered as mutually exclusive classes and standard NER methods like sequence tagging could be used. For Entity Retrieval, Roles could be formulated as Query and entities as Collection on which the query needs to be executed. The aspect of Entity Retrieval task, which is different than document retrieval task is that the entities and roles against which they need to be retrieved are indirectly described. We have formulated automated ways of learning representative words and phrases and building representations of roles and entities using them. We have also explored different contexts like sentence and document. Since the roles depend upon context, so it is not always possible to have large domain-specific dataset or knowledge bases for learning purposes, so we have tried to exploit the information from small dataset in domain-agnostic way.

GQA GQA has annotations of objects and attributes in images. We use this to construct queries like "square white plate". We train on the GQA train split (with the test unseen queries and corresponding images removed). Hence, we have around 67K training images and 27K queries. CLEVR On CLEVR, we test on 96 classes on 22,500 images.