*About Vivoka*

Founded in 2015 and awarded two CES Innovation Awards, Vivoka
(https://vivoka.com/en/) has created and sells the Voice Development
Kit (VDK), the very first solution allowing a company to design a voice
interface in a simple, autonomous, and quick way. Moreover, this
interface is embedded: it can be deployed on devices without an
Internet connection and fully preserves privacy. Accelerated by the
COVID-19 health crisis and the need for "no-touch" interfaces, Vivoka
is now optimizing this technology by developing its own speech and
language processing solutions that are able to compete with the most
efficient current technologies. The internship would be carried out as
part of Vivoka's R&D team. The interns will benefit from the startup
spirit of Vivoka, where they will interact with the researchers and
Ph.D. students of the R&D team, and the engineers responsible for
integrating their results into the VDK.


Internship Requirements:

    -   M2 in Computer Science with a specialization in Machine
        Learning (ML) or Natural Language Processing (NLP)
    -   Prior knowledge and/or experience with ML/NLP.
    -   Experience with Python programming and frameworks like PyTorch.


1.  Robust Dialogue State Tracking for Dialog Management in
    Conversational AI

Context

Conversational systems improve user experience by steering interactions
to understand users' needs and respond by providing informed answers,
assistance, invoking services, etc. Unlike non-task-oriented dialogue
systems that focus on open-domain conversations, such as chit-chats,
task-oriented conversational systems enable users to accomplish certain
tasks using the information provided during conversations. One of the
critical aspects of conversational systems is the design of dialogue
management that allows robust, intelligent, engaging conversations [1,
2, 3]. The focus of this internship is dialogue management in
task-oriented conversational systems.

In task-oriented dialogue systems, the dialogue state is the component
of a dialogue manager that serves as a summary of the entire
conversation up to the present turn. It maintains all the essential
information that the system needs to give informed responses to the
user's queries. This information comprises mainly the user's intents
(e.g. flight_booking), slots, i.e. information needed to fulfill the
intent (e.g. departure and arrival cities), and dialogue acts, i.e.
hidden actions in user utterances to indicate their specific
communicative function (e.g. request, statement, etc.) [3]. The
dialogue states are estimated and tracked by the Dialogue State
Tracking (DST) model [4]. Based on the dialogue states, the
conversational agent generates subsequent actions to sustain the
ongoing conversation. In real-world conversations, the range of
potential values for slots is often dynamic and unbounded, such as
movie_titles or usernames. Consequently, in recent years, there has
been an active focus on open-vocabulary approaches to DST [3]. These
approaches involve estimating the possible values for slots from the
ongoing conversation and language understanding results, without
relying on a predefined set of categories. This research area
represents a critical advancement toward DST with zero-shot
generalization, which means that adding new intents and slots can be
achieved without the need for collecting new data or extensive
retraining.

This internship aims to explore dialogue management in conversational
systems with a particular focus on robust DST approaches that can
achieve few-shot or zero-shot generalization. In real use cases, the
disfluent nature of spontaneous conversations poses an additional set
of challenges for Dialogue Management. The internship will focus on the
challenges that are encountered while building robust task-oriented DST
approaches meant for real-world applications of conversational systems.


Objectives and Expected Outcomes:
    -   Perform a literature review of Dialogue Management
    -   Implement a state-of-the-art Dialogue State Tracking approach
        in PyTorch
    -   Improve the implemented DST approach to perform few/zero-shot
        generalization
    -   Perform experiments to examine the challenges with real-world
        conversations for dialogue management
    -   Perform experiments to examine the generalizability of the
        implemented DST approach


*References:*

    1.  M. McTear, Z. Callejas, and D. Griol, "The Conversational
        Interface: Talking to Smart Devices"
        https://link.springer.com/book/10.1007/978-3-319-32967-3,
        1st ed. Springer Publishing Company, Incorporated, 2016.
    2.  Z. Zhang, M. Huang, Z. Zhao, F. Ji, H. Chen, and X. Zhu,
        "Memory-augmented dialogue management for task-oriented
        dialogue systems"
        https://dl.acm.org/doi/abs/10.1145/3317612,
        ACM Transactions on Information Systems (TOIS), 2019.
    3.  H. Brabra, M. Báez, B. Benatallah, W. Gaaloul, S. Bouguelia
        and S. Zamanirad, "Dialogue Management in Conversational
        Systems: A Review of Approaches, Challenges, and
        Opportunities"
        https://ieeexplore.ieee.org/document/9447005, in IEEE
        Transactions on Cognitive and Developmental Systems,
        vol. 14, no. 3, pp. 783-798, 2022
    4.  Jason Williams, Antoine Raux, Deepak Ramachandran, and Alan
        Black. 2013. "The Dialog State Tracking Challenge"
        https://aclanthology.org/W13-4065/.
        In Proceedings of the SIGDIAL 2013 Conference, pages 404-413,
        Association for Computational Linguistics, 2013.

Please submit your applications to tulika.bose@vivoka.com or
firas.hmida@vivoka.com. Please feel free to share this call for
applications with any interested students.