Arpan Saxena

Amber Nigam, Aakash Roy, Arpan Saxena et al.

Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection by candidates using machine learning. Additionally, our recommendation is composed of several other sub-recommendations that contribute to at least one of a) making recommendations serendipitous for the end user b) overcoming cold-start for both candidates and jobs. One of the unique selling propositions of our methodology is the way we have used skills as embedded features and derived latent competencies from them, thereby attempting to expand the skills of candidates and jobs to achieve more coverage in the skill domain. We have deployed our model in a real-world job recommender system and have achieved the best click-through rate through a blended approach of machine-learned recommendations and other sub-recommendations. For recommending jobs through machine learning that forms a significant part of our recommendation, we achieve the best results through Bi-LSTM with attention.

Amber Nigam, Arpan Saxena, Ishan Sodhi

In this paper, we have introduced and evaluated intonation based feature for scoring the English speech of nonnative English speakers in Indian context. For this, we created an automated spoken English scoring engine to learn from the manual evaluation of spoken English. This involved using an existing Automatic Speech Recognition (ASR) engine to convert the speech to text. Thereafter, macro features like accuracy, fluency and prosodic features were used to build a scoring model. In the process, we introduced SimIntonation, short for similarity between spoken intonation pattern and "ideal" i.e. training intonation pattern. Our results show that it is a highly predictive feature under controlled environment. We also categorized interword pauses into 4 distinct types for a granular evaluation of pauses and their impact on speech evaluation. Moreover, we took steps to moderate test difficulty through its evaluation across parameters like difficult word count, average sentence readability and lexical density. Our results show that macro features like accuracy and intonation, and micro features like pause-topography are strongly predictive. The scoring of spoken English is not within the purview of this paper.