Abstract
The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches target the development of multi-lingual tools and models, they still underperform in languages such as Portuguese, for which linguistic resources do not abound. This paper exposes a set of challenges encountered when dealing with a real-world complex NLP problem, based on user-generated complaint data in Portuguese. This case study meets the needs of a country-wide governmental institution responsible for food safety and economic surveillance, and its responsibilities in handling a high number of citizen complaints. Beyond looking at the problem from an exclusively academic point of view, we adopt application-level concerns when analyzing the progress obtained through different techniques, including the need to obtain explainable decision support. We discuss modeling choices and provide useful insights for researchers working on similar problems or data.
Highlights
The usage of Artificial intelligence (AI) technologies is widespread in virtually every sector of human activity
By providing public services through virtual counters, governmental institutions are often required to respond to large numbers of citizen contacts, a process that may quickly become intractable, depending on the size of the country or administrative region covered by the institution
Automating complaint handling is a challenging task. This paper addresses this real-world problem, considering complaint handling in three separate classification tasks that are related to the internal processing of complaints within
Summary
The usage of Artificial intelligence (AI) technologies is widespread in virtually every sector of human activity. Public administration institutions and governments seek to take advantage of AI to deal with specific needs and opportunities related to their access to substantial amounts of both structured and unstructured information. Natural language processing (NLP) techniques are being used to handle both web-originated text (such as in social networks or newswire) and, most importantly, written information produced in the process of an ever more direct interaction between citizens and governmental institutions [1]. By providing public services through virtual counters, governmental institutions are often required to respond to large numbers of citizen contacts (such as requests or complaints), a process that may quickly become intractable, depending on the size of the country or administrative region covered by the institution. NLP techniques can help address this information overload and improve public services [2] by automating the processing of textual data
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