Page 210 - Artificial Intelligence for the Internet of Everything

194 Artificial Intelligence for the Internet of Everything

Fig. 11.2A) is labeled with say(Dutch, evidence), and the satellite is labeled
with responsible(rebels, shooting_down). These labels are not intended to
express that the subjects of elementary discourse units (EDUs) are evidence
and shooting_down but instead are intended for matching this CDT with
others for the purpose of finding similarity between them.
Notice that in the CDTs for three paragraphs expressing the views of
conflicting parties (Fig. 11.2A–C), communicative actions with their sub-
jects contain the main claims of the respective party, and the DTs without
these labels contain information on how these claims are logically packaged.
To summarize, a typical CDT for a text with argumentation includes rhe-
toric relations other than “elaboration” and “join,” and a substantial number
of communicative actions. However, these rules are complex enough so that
the structure of CDT matters and tree-specific learning is required (Galitsky,
Ilvovsky, & Kuznetsov, 2015).

11.3 DETECTING INVALID ARGUMENTATION PATTERNS

Starting from the autumn of 2015, we became interested in the controversy
about Theranos, the health-care company that hoped to make a revolution
in blood tests. Some sources including the Wall Street Journal started claiming
that the company’s conduct was fraudulent. The claims were made based on
the whistleblowing of employees who left Theranos. At some point the US
Food and Drug Administration got involved, and as the case developed, we
were improving our argumentation mining and reasoning techniques
(Galitsky, 2016; Galitsky, Ilvovsky, & Kuznetsov, 2018) while keeping an
eye on Theranos’ story. As we scraped from the websites the discussions
about Theranos back in 2016, the audience believed that the case was ini-
tiated by Theranos’ competitors who felt jealous about the proposed effi-
ciency of the blood test technique promised by Theranos. However, our
argumentation analysis technique was showing that Theranos’ argumenta-
tion patterns mined at their website were faulty and our finding confirmed
the case, which led to the massive fraud verdict. SEC (2018) says that Ther-
anos’ CEO Elizabeth Holmes raised more than $700 million from investors
“through an elaborate, years-long fraud” in which she exaggerated or made
false statements about the company’s technology and finances.
Let us imagine that we need to index the content about Theranos for
answering questions about it. If a user leans towards Theranos and not its
opponents, then we want to provide answers favoring Theranos’ position.
Good arguments of its proponents, or bad arguments of its opponents would
also be good. Table 11.1 shows the flags for various combinations of agency,