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Objectives To examine the effect of premorbid β-blocker exposure on mortality and organ dysfunction in sepsis. Design Retrospective observational study. Setting ICUs in Australia, the Czech Republic, and the United States. Patients Total of 4,086 critical care patients above 18 years old with sepsis between January 2014 and December 2018. Intervention Premorbid beta-blocker exposure. Measurements and main results One thousand five hundred fifty-six patients (38%) with premorbid β-blocker exposure were identified. Overall ICU mortality rate was 15.1%. In adjusted models, premorbid β-blocker exposure was associated with decreased ICU (adjusted odds ratio, 0.80; 95% CI, 0.66-0.97; p = 0.025) and hospital (adjusted odds ratio, 0.83; 95% CI, 0.71-0.99; p = 0.033) mortality. The risk reduction in ICU mortality of 16% was significant (hazard ratio, 0.84, 95% CI, 0.71-0.99; p = 0.037). In particular, exposure to noncardioselective β-blocker before septic episode was associated with decreased mortality. Sequential Organ Failure Assessment score analysis showed that premorbid β-blocker exposure had potential benefits in reducing respiratory and neurologic dysfunction. Conclusions This study suggests that β-blocker exposure prior to sepsis, especially to noncardioselective β blockers, may be associated with better outcome. The findings suggest prospective evaluation of β-blocker use in the management of sepsis.

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart., NAACL 2021

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit., Comment: ACL 2020

We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets:(1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot-value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https://yale-lily.github.io/cosql., Comment: Accepted to EMNLP 2019, long paper