Page 228 - Artificial Intelligence for the Internet of Everything

Accessing Validity of Argumentation of Agents of the Internet of Everything 209

11.6 INTENSE ARGUMENTS DATASET

The purpose of this dataset is to collect texts where authors do their best to
bring their points across by employing all means to show that they are right
and their opponents are wrong. Complainants are emotionally charged
writers who describe the problems they have encountered with a financial
service and how they attempted to solve them.
Most complaint authors report incompetence, flawed policies, igno-
rance, indifference to customer needs, and misrepresentation from the cus-
tomer service personnel (Galitsky, Gonza ´lez, & Chesn ˜evar, 2009). The
focus of a complaint is a proof that the proponent is right and his/her oppo-
nent is wrong, followed by a resolution proposal and a desired outcome.
Complaints revealed the shady practices of banks during the financial
crisis of 2007, such as manipulating an order of transactions to charge the
highest possible amount of insufficient fund fees. Moreover, banks
attempted to communicate this practice as a necessity to process a wide
amount of checks. This is the most frequent topic of customer complaints,
so one can track a manifold of argumentation patterns applied to this topic.
For a given topic, such as insufficient funds fee, this dataset provides many
distinct ways of argumentation that this fee is unfair. Therefore our
dataset allows for systematic exploration of the topic-independent clusters
of argumentation patterns to observe a link between argumentation type
and overall complaint validity. Other argumentation datasets, including legal
arguments, student essays, Internet argument corpus, fact-feeling, and polit-
ical debates, have a strong variation of topics so it is harder to track a spec-
trum of possible argumentation patterns per topic. Unlike professional
writing in legal and political domains, the messages produced by complain-
ants have a simple motivational structure, a transparency of their purpose,
and occur in a fixed domain and context. In our dataset, the affective argu-
ments play a critical rule for the well-being of authors subject to an unfair
charge of a large amount of money or eviction from their home. Therefore
the authors attempt to provide as strong argumentation as possible to back up
their claims and strengthen their cases.

11.7 EVALUATION OF DETECTION AND VALIDATION
OF ARGUMENTS

The objective of argument detection task is to identify all kinds of argu-
ments, not only the ones associated with customer complaints. We formed