Maria Gini


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A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
Zhecheng Sheng | Raymond Finzel | Michael Lucke | Sheena Dufresne | Maria Gini | Serguei Pakhomov
Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

In healthcare, the ability to care for oneself is reflected in the “Activities of Daily Living (ADL),” which serve as a measure of functional ability (functioning). A lack of functioning may lead to poor living conditions requiring personal care and assistance. To accurately identify those in need of support, assistance programs continuously evaluate participants’ functioning across various domains. However, the assessment process may encounter consistency issues when multiple assessors with varying levels of expertise are involved. Novice assessors, in particular, may lack the necessary preparation for real-world interactions with participants. To address this issue, we developed a dialogue system that simulates interactions between assessors and individuals of varying functioning in a natural and reproducible way. The dialogue system consists of two major modules, one for natural language understanding (NLU) and one for natural language generation (NLG), respectively. In order to generate responses consistent with the underlying knowledge base, the dialogue system requires both an understanding of the user’s query and of biographical details of an individual being simulated. To fulfill this requirement, we experimented with query classification and generated responses based on those biographical details using some recently released InstructGPT-like models.

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Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets
Risako Owan | Maria Gini | Dongyeop Kang
Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

Modal verbs, such as can, may, and must, are commonly used in daily communication to convey the speaker’s perspective related to the likelihood and/or mode of the proposition. They can differ greatly in meaning depending on how they’re used and the context of a sentence (e.g. “They must help each other out.” vs. “They must have helped each other out.”). Despite their practical importance in natural language understanding, linguists have yet to agree on a single, prominent framework for the categorization of modal verb senses. This lack of agreement stems from high degrees of flexibility and polysemy from the modal verbs, making it more difficult for researchers to incorporate insights from this family of words into their work. As a tool to help navigate this issue, this work presents MoVerb, a dataset consisting of 27,240 annotations of modal verb senses over 4,540 utterances containing one or more sentences from social conversations. Each utterance is annotated by three annotators using two different theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses. We observe that both frameworks have similar inter-annotator agreements, despite having a different number of sense labels (eight for Quirk and three for Palmer). With RoBERTa-based classifiers fine-tuned on MoVerb, we achieve F1 scores of 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verb sense disambiguation is not a trivial task.


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Everyday Living Artificial Intelligence Hub
Raymond Finzel | Esha Singh | Martin Michalowski | Maria Gini | Serguei Pakhomov
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

We present the Everyday Living Artificial Intelligence (AI) Hub, a novel proof-of-concept framework for enhancing human health and wellbeing via a combination of tailored wear-able and Conversational Agent (CA) solutions for non-invasive monitoring of physiological signals, assessment of behaviors through unobtrusive wearable devices, and the provision of personalized interventions to reduce stress and anxiety. We utilize recent advancements and industry standards in the Internet of Things (IoT)and AI technologies to develop this proof-of-concept framework.

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Conversational Agent for Daily Living Assessment Coaching Demo
Raymond Finzel | Aditya Gaydhani | Sheena Dufresne | Maria Gini | Serguei Pakhomov
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Conversational Agent for Daily Living Assessment Coaching (CADLAC) is a multi-modal conversational agent system designed to impersonate “individuals” with various levels of ability in activities of daily living (ADLs: e.g., dressing, bathing, mobility, etc.) for use in training professional assessors how to conduct interviews to determine one’s level of functioning. The system is implemented on the MindMeld platform for conversational AI and features a Bidirectional Long Short-Term Memory topic tracker that allows the agent to navigate conversations spanning 18 different ADL domains, a dialogue manager that interfaces with a database of over 10,000 historical ADL assessments, a rule-based Natural Language Generation (NLG) module, and a pre-trained open-domain conversational sub-agent (based on GPT-2) for handling conversation turns outside of the 18 ADL domains. CADLAC is delivered via state-of-the-art web frameworks to handle multiple conversations and users simultaneously and is enabled with voice interface. The paper includes a description of the system design and evaluation of individual components followed by a brief discussion of current limitations and next steps.