Expert recommendation based on social drivers, social network analysis, and semantic data representation

ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration

Scaling Speech Technology to 1,000+ Languages

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pre-training stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation

Representing and Reasoning about Skills and Competencies over Time

To stay competitive within the market, organizations need to accurately understand the skills and competencies of their human resources to better utilize them and more effectively respond to internal and external demands for expertise. This is particularly important for most services organizations which provide a variety of products and services to multiple and changing clients. The ability to accurately locate experts is also important to knowledge workers. From this perspective, finding individuals with appropriate knowledge and skills is important for accomplishing knowledge intensive tasks and solving complex problems. This thesis focuses on the problem of representing and reasoning about skills and competencies over time for more effective human resources management and expert finding. Proper modeling of skills and competencies provides, among other things, a common language for assessments, a foundation for consistent interviewing, a linkage between performance management and learning, and a means for aligning business strategy and skills for driving organizational performance. It also improves knowledge management by making explicit what the organization knows and can perform. In this thesis, we present a framework for profiling human resources over time. In particular, we use first-order logic to represent and reason about expertise, skills and competencies and capture information about sources of skill and competency information. The framework provides: - a formal ontology for skill and competency management which specifies how individuals should be represented and evaluated against a skill, - a technique for inferring and validating skills and competencies over time using different sources of information, - a systematic way of evaluating human resources in order to provide a more efficient, structured, and consistent assessment process, and - techniques for identifying unreliable sources of information and revising trust in these sources. This work enhances the ability of organizations to better utilize their human assets and improves the task of expert finding required for accomplishing knowledge intensive tasks and solving complex problems.Ph