AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.Comment: ACL 2023 Industry Trac

Identifying and Describing Information Seeking Tasks

A software developer works on many tasks per day, frequently switching between these tasks back and forth. This constant churn of tasks makes it difficult for a developer to know the specifics of when they worked on what task, complicating task resumption, planning, retrospection, and reporting activities. In a first step towards an automated aid to this issue, we introduce a new approach to help identify the topic of work during an information seeking task — one of the most common types of tasks that software developers face — that is based on capturing the contents of the developer’s active window at regular intervals and creating a vector representation of key information the developer viewed. To evaluate our approach, we created a data set with multiple developers working on the same set of six information seeking tasks that we also make available for other researchers to investigate similar approaches. Our analysis shows that our approach enables: 1) segments of a developer’s work to be automatically associated with a task from a known set of tasks with average accuracy of 70.6%, and 2) a word cloud describing a segment of work that a developer can use to recognize a task with average accuracy of 67.9%

Identifying the Linguistic Correlates of Rhetorical Relations

RASTA (Rhetorical Structure Theory Analyzer), a system for automatic discourse analysis, reliably. identifies rhetorical relations present m written discourse by examining information available in syntactic and logical form analyses. Since there is a many-to-many relationship between rhetorical relations and elements of linguistic form, RASTA identifies relations by the convergence of a number of pieces of evidence, many of which would be insufficient in isolation to reliably identify a relation

Beyond string matching and cue phrases: Improving efficiency and coverage in discourse analysis

RASTA (Rhetorical Structure Theory Analyzer), a discourse analysis component within the Microsoft English Grammar, efficiently computes representations of the structure of written discourse using information available in syntactic and logical form analyses. RASTA heuristically scores the rhetorical relations that it hypothesizes, using those scores to guide it in producing more plausible discourse representations before less plausible ones. The heuristic scores also provide a genre-independent method for evaluating competing discourse analyses: the best discourse analyses are those constructed from the strongest hypotheses

Dependency Parsing with Reference to Slovene, Spanish and Swedish

We describe a parser used in the CoNLL 2006 Shared Task, “Multingual Dependency Parsing. ” The parser first identifies syntactic dependencies and then labels those dependencies using a maximum entropy classifier. We consider the impact of feature engineering and the choice of machine learning algorithm, with particula

Normalizing German and English inflectional morphology to improve statistical word alignment

Abstract. German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors.