KILT: a Benchmark for Knowledge Intensive Language Tasks.

Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearc

How Context Affects Language Models' Factual Predictions

When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering. However, storing factual knowledge in a fixed number of weights of a language model clearly has limitations. Previous approaches have successfully provided access to information outside the model weights using supervised architectures that combine an information retrieval system with a machine reading component. In this paper, we go a step further and integrate information from a retrieval system with a pre-trained language model in a purely unsupervised way. We report that augmenting pre-trained language models in this way dramatically improves performance and that the resulting system, despite being unsupervised, is competitive with a supervised machine reading baseline. Furthermore, processing query and context with different segment tokens allows BERT to utilize its Next Sentence Prediction pre-trained classifier to determine whether the context is relevant or not, substantially improving BERT’s zeroshot cloze-style question-answering performance and making its predictions robust to noisy contexts

Budget Allocation for Customer Acquisition and Retention to Balance Market Share Growth and Customer Profitability

[[abstract]]Following the Blattberg and Deighton (BD) model, we incorporate market share growth to explore links between acquisition and retention. We then devise a method for nonlinear programming using a spreadsheet to balance the objectives of market share growth in the short term and customer equity in the long term. The aim of this approach is to determine the optimal spending allocation for customer acquisition and retention and, by applying this allocation to the numerical example used in the original BD model, to balance these objectives. We demonstrate that the differential unit cost of marginal effects, ceiling rate, efficiency, and allocation of spending on acquisition and retention to achieve market share growth can maximize customer equity. We also develop a criterion to help firms decide where to place spending emphasis, that is, on retaining existing customers or on gaining new ones, while keeping the objectives of market share growth and customer equity firmly in mind.[[incitationindex]]SSCI[[booktype]]電子

Do vendors benefit from promotions in a multi-vendor loyalty program?

Loyalty programs, Multi-vendor loyalty program, Promotion effectiveness, Joint promotions, Cross-vendor effects,