Benjamin Searle

[...] Today’s cutting edge IR systems are not fundamentally different than classical IR systems developed many decades ago. Indeed, a majority of today’s systems boil down to: (a) building an efficient queryable index for each document in the corpus, (b) retrieving a set of candidates for a given query, and (c) computing a relevance score for each ... See more

[Curator's note: there are numerous other technical challenges  addressed with alternative prescriptions throughout the paper. These highlights are narrative-centric, and I invite you to review the paper if you are a keen technologist looking for answers to the following:- Zero- and Few-Shot Learning - Response Generation- Arith... See more

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval (IR) system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers.

We envision using the same corpus model as a multi-task learner for multiple IR tasks. To this end, once a corpus model has been trained, it can of course be used for the most classical of all IR tasks – document retrieval. However, by leveraging recent advances in multi-task learning, such a model can very likely be applied to a diverse range of t... See more