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Deep language models learning a hierarchical representation proved to be apowerful tool for natural language processing, text mining and informationretrieval. However, representations that perform well for retrieval mustcapture semantic meaning at different levels of abstraction or context-scopes.In this paper, we propose a new method to generate multi-resolution wordembeddings that represent documents at multiple resolutions in terms ofcontext-scopes. In order to investigate its performance,we use the StanfordQuestion Answering Dataset (SQuAD) and the Question Answering by Search AndReading (QUASAR) in an open-domain question-answering setting, where the firsttask is to find documents useful for answering a given question. To this end,we first compare the quality of various text-embedding methods for retrievalperformance and give an extensive empirical comparison with the performance ofvarious non-augmented base embeddings with and without multi-resolutionrepresentation. We argue that multi-resolution word embeddings are consistentlysuperior to the original counterparts and deep residual neural modelsspecifically trained for retrieval purposes can yield further significant gainswhen they are used for augmenting those embeddings.

A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases