3 research outputs found
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
A major concern in using deep learning based generative models for
document-grounded dialogs is the potential generation of responses that are not
\textit{faithful} to the underlying document. Existing automated metrics used
for evaluating the faithfulness of response with respect to the grounding
document measure the degree of similarity between the generated response and
the document's content. However, these automated metrics are far from being
well aligned with human judgments. Therefore, to improve the measurement of
faithfulness, we propose a new metric that utilizes (Conditional) Point-wise
Mutual Information (PMI) between the generated response and the source
document, conditioned on the dialogue. PMI quantifies the extent to which the
document influences the generated response -- with a higher PMI indicating a
more faithful response. We build upon this idea to create a new decoding
technique that incorporates PMI into the response generation process to predict
more faithful responses. Our experiments on the BEGIN benchmark demonstrate an
improved correlation of our metric with human evaluation. We also show that our
decoding technique is effective in generating more faithful responses when
compared to standard decoding techniques on a set of publicly available
document-grounded dialog datasets.Comment: EMNLP 202
International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module
We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN