What Set of Documents to Present to an Analyst?

Abstract

We describe the human triage scenario envisioned in the Cross-Lingual Information Retrieval (CLIR) problem of the IARPA MATERIAL Program. The overall goal is to maximize the quality of the set of documents that is given to a bilingual analyst, as measured by the AQWV score. The initial set of source documents that are retrieved by the CLIR system is summarized in English and presented to human judges who attempt to remove the irrelevant documents (false alarms); the resulting documents are then presented to the analyst. First, we describe the AQWV performance measure and show that, in our experience, if the acceptance threshold of the CLIR component has been optimized to maximize AQWV, the loss in AQWV due to false alarms is relatively constant across many conditions, which also limits the possible gain that can be achieved by any post filter (such as human judgments) that removes false alarms. Second, we analyze the likely benefits for the triage operation as a function of the initial CLIR AQWV score and the ability of the human judges to remove false alarms without removing relevant documents. Third, we demonstrate that we can increase the benefit for human judgments by combining the human judgment scores with the original document scores returned by the automatic CLIR system.

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Document Details

Document Type
Technical Report
Publication Date
May 11, 2020
Accession Number
AD1149891

Entities

People

  • Damianos Karakos
  • John Makhoul
  • Lee Tarlin
  • Richard Schwartz

Organizations

  • RTX

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Automated Speech Recognition
  • Automatic
  • Czech Republic
  • English Language
  • False Alarms
  • Foreign Languages
  • Information Retrieval
  • Judgment
  • Language
  • Materials
  • Military Research
  • Precision
  • Rejection
  • Signal Processing
  • Warning Systems

Readers

  • Computational Linguistics
  • Sensor Fusion and Tracking Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Translation