Enabling Deep Learning for Multilingual Sociopragmatics

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Natural language processing (NLP) is the exciting field focused at teaching computers to understand and generate human language. Recently, deep learning, a class of machine learning methods inspired by information processing in the human brain, has broken records on many NLP tasks for which large amounts of labeled data are available (e.g., machine translation, speech recognition). Due to these advances and the pervasive technologies it enables, deep learning of natural language is currently a strategic area of high socioeconomic impact. This makes it ripe time for creating models that understand human language at the level of sociopragmatics (i.e., the meaning of a proposition depends on the social context in which it is uttered). Many challenges, however, remain. Two prominent, inter-related, examples are (a) the high costs associated to labeling data, and (b) the bias in existing labeled data. Absence of labeled data hinders progress on building powerful deep learning models since these models scale exclusively given large amounts of labeled data. Biased labeled data result in creating technologies that serve particular dominant groups better than others, which can have serious social and economic repercussions.******My research program aims at developing methods to accelerate deep learning of natural language at the level of sociopragmatics by targeting these two core problems, with a focus on bringing NLP technologies to wider demographics across several languages and language varieties. ******The proposal has three key objectives: ******1. Cross-Lingual Surrogate Labeling: This involves developing methods for automatically labeling data for sociopragmatic tasks (e.g., user intention modeling, empathy detection), with a focus on English and all Arabic varieties (i.e., varieties representing all the 22 Arab countries). ******2. Deep Generative Semi-Supervised Learning: I will develop methods that exploit deep generative models, a class of deep learning methods that can generate sensible language that can be leveraged as labeled data. This will help solve the two data-focused problems above (i.e., a and b). ******3. Toward Social Machines With Controlled Sociopragmatics: My goal is to develop sociopragmatically intelligent conversational models capable of dynamic customization in response to conversant attributes (e.g., emotionally intelligent language generation, gender- and personality-specific conversational agents).******The research will have a wide range of applications in various fields, including decision making, health and well-being, education, recreation, and entertainment. Since it is a specialized subfield at the junction of a number of already supply-constrained fields, deep learning of natural language currently suffers from acute shortage of talent. HQP training provided by my program will contribute to fulfilling these ever-rising needs.