Anumeha Agrawal


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Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback
Anumeha Agrawal | Rosa Anil George | Selvan Sunitha Ravi | Sowmya Kamath S | Anand Kumar
Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)

Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee’s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Candidates, therefore, need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee’s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.


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ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System
Anumeha Agrawal | Rosa Anil George | Selvan Suntiha Ravi | Sowmya Kamath S | Anand Kumar
Proceedings of the 18th BioNLP Workshop and Shared Task

In this paper, we present three approaches for Natural Language Inference, Question Entailment Recognition and Question-Answering to improve domain-specific Information Retrieval. For addressing the NLI task, the UMLS Metathesaurus was used to find the synonyms of medical terms in given sentences, on which the InferSent model was trained to predict if the given sentence is an entailment, contradictory or neutral. We also introduce a new Extreme gradient boosting model built on PubMed embeddings to perform RQE. Further, a closed-domain Question Answering technique that uses Bi-directional LSTMs trained on the SquAD dataset to determine relevant ranks of answers for a given question is also discussed.