Companion-Technology for Cognitive Technical Systems

What is Missing in XAI So Far?

With the perspective on applications of AI-technology, especially data intensive deep learning approaches, the need for methods to control and understand such models has been recognized and gave rise to a new research domain labeled explainable artificial intelligence (XAI). In this overview paper we give an interim appraisal of what has been achieved so far and where there are still gaps in the research. We take an interdisciplinary perspective to identify challenges on XAI research and point to open questions with respect to the quality of the explanations regarding faithfulness and consistency of explanations. On the other hand we see a need regarding the interaction between XAI and user to allow for adaptability to specific information needs and explanatory dialog for informed decision making as well as the possibility to correct models and explanations by interaction. This endeavor requires an integrated interdisciplinary perspective and rigorous approaches to empirical evaluation based on psychological, linguistic and even sociological theories.

Acoustic-Based Automatic Addressee Detection for Technical Systems: A Review

Objective

Acoustic addressee detection is a challenge that arises in human group interactions, as well as in interactions with technical systems. The research domain is relatively new, and no structured review is available. Especially due to the recent growth of usage of voice assistants, this topic received increased attention. To allow a natural interaction on the same level as human interactions, many studies focused on the acoustic analyses of speech. The aim of this survey is to give an overview on the different studies and compare them in terms of utilized features, datasets, as well as classification architectures, which has so far been not conducted.

Methods

The survey followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We included all studies which were analyzing acoustic and/or acoustic characteristics of speech utterances to automatically detect the addressee. For each study, we describe the used dataset, feature set, classification architecture, performance, and other relevant findings.

Results

1,581 studies were screened, of which 23 studies met the inclusion criteria. The majority of studies utilized German or English speech corpora. Twenty-six percent of the studies were tested on in-house datasets, where only limited information is available. Nearly 40% of the studies employed hand-crafted feature sets, the other studies mostly rely on Interspeech ComParE 2013 feature set or Log-FilterBank Energy and Log Energy of Short-Time Fourier Transform features. 12 out of 23 studies used deep-learning approaches, the other 11 studies used classical machine learning methods. Nine out of 23 studies furthermore employed a classifier fusion.

Conclusion

Speech-based automatic addressee detection is a relatively new research domain. Especially by using vast amounts of material or sophisticated models, device-directed speech is distinguished from non-device-directed speech. Furthermore, a clear distinction between in-house datasets and pre-existing ones can be drawn and a clear trend toward pre-defined larger feature sets (with partly used feature selection methods) is apparent.

A Review of Plan-Based Approaches for Dialogue Management

Dialogue systems deliver a more natural mean of communication between humans and machines when compared to traditional systems. Beyond input/output components that understand and generate natural language utterances, the core of a dialogue system is the dialogue manager. The aim of the dialogue manager is to mimic all cognitive aspects related to a natural conversation and it is responsible for identifying the current state of the dialogue and for deciding the next action to be taken by a dialogue system. Artificial intelligence (AI) planning is one of the techniques available in the literature for dialogue management. In a dialogue system, AI planning deals with the action selection problem by treating each utterance as an action and by choosing the actions that get closer to the dialogue goal. This work aims to provide a systematic literature review (SLR) that investigates recent contributions to plan-based dialogue management. This SLR aims at answering research questions concerning: (i) the types of AI planning exploited for dialogue management; (ii) the planning characteristics that justify its adoption in dialogue system; (iii) and, the challenges posed on the development of plan-based dialogue managers. The present SLR was performed by querying four scientific repositories, followed by a manual search on works from the most eminent authors in the field. Further works that were cited by the retrieved papers were also considered for inclusion. Our final corpus is composed of forty works, including only works published since 2014. The results indicate that AI planning is still an emerging strategy for dialogue management. Although AI planning can offer a strong contribution to dialogue systems, especially to those that require predictability, some relevant challenges might still limit its adoption. Our results contributed to discussions in the field and they highlight some research gaps to be addressed in future studies.