Users an organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private context is important to personalize open-domain tasks such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve information that is relevant to an input question from a background corpus before producing an answer. While today’s retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We define the Split Iterative Retrieval (SPIRAL) problem involving iterative retrieval over multiple privacy scopes. We introduce a foundational benchmark with which to study SPIRAL, as no existing benchmark includes data from a private distribution. Our dataset, ConcurrentQA, includes data from distinct public and private distributions and is the first textual QA benchmark requiring concurrent retrieval over multiple distributions. Finally, we show that existing retrieval approaches face significant performance degradations when applied to our proposed retrieval setting and investigate approaches with which these tradeoffs can be mitigated. We release the new benchmark and code to reproduce the results.1

Popular language models (LMs) struggle to capture knowledge about rare tail facts and entities. Since widely used systems such as search and personal-assistants must support the long tail of entities that users ask about, there has been significant effort towards enhancing these base LMs with factual knowledge. We observe proposed methods typically start with a base LM and data that has been annotated with entity metadata, then change the model, by modifying the architecture or introducing auxiliary loss terms to better capture entity knowledge. In this work, we question this typical process and ask to what extent can we match the quality of model modifications, with a simple alternative: using a base LM and only changing the data. We propose metadata shaping, a method which inserts substrings corresponding to the readily available entity metadata, e.g. types and descriptions, into examples at train and inference time based on mutual information. Despite its simplicity, metadata shaping is quite effective. On standard evaluation benchmarks for knowledge-enhanced LMs, the method exceeds the base-LM baseline by an average of 4.3 F1 points and achieves state-of-the-art results. We further show the gains are on average 4.4x larger for the slice of examples containing tail vs. popular entities.

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline—random word embeddings—focusing on the impact of the training set size and the linguistic properties of the task. Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10% accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.