Selecting an appropriate and relevant context forms an essential component for the efficacy of several information retrieval applications like Question Answering (QA) systems. The problem of Answer Sentence Selection (AS2) refers to the task of selecting sentences, from a larger text, that are relevant and contain the answer to users' queries. While there has been a lot of success in building AS2 systems trained on open-domain data (e.g., SQuAD, NQ), they do not generalize well in closed-domain settings, since domain adaptation can be challenging due to poor availability and annotation expense of domain-specific data. This paper proposes SEDAN, an effective self-learning framework to adapt AS2 models for domain-specific applications. We leverage large pre-trained language models to automatically generate domain-specific QA pairs for domain adaptation. We further fine-tune a pre-trained Sentence-BERT architecture to capture semantic relatedness between questions and answer sentences for AS2. Extensive experiments demonstrate the effectiveness of our proposed approach (over existing state-of-the-art AS2 baselines) on different Question Answering benchmark datasets.

Question Answering (QA) has become a foundational research area in Natural Language Understanding (NLU) with widespread applications in search, personal digital assistance, and conversational systems. Despite the success in open-domain question answering, existing extractive question answering models pre-trained using Wikipedia articles (e.g., SQuAD data) perform rather poorly in closed-domain and industrial scenarios. Further, a major limitation in adapting question answering systems to such contexts is the poor availability and the expensive annotation of domain-specific data. Thus, wide applicability of QA models are severely hampered in enterprise systems.In this paper, we aim to address the above challenges by introducing a novel QA framework, Qasar, using self-supervised learning for efficient domain adaptation. We show, for the first time, the advantage of fine-tuning pre-trained QA models for closed-domains by synthetically generated domain-specific questions and answers (from relevant documents) from large language models like T5. Further, we also propose a novel context retrieval component based on question-context semantic relatedness to further boost the accuracy of the Qasar QA framework. Experimental results show significant performance improvements on both open-and closed-domain QA datasets, while requiring no labelling efforts, which we believe will contribute to the ease of deployment of such systems in enterprise settings. The different modules of our framework (synthetic data generation, context retrieval, and question answering) can be fully reproduced by fine-tuning publicly available language models and QA models on SQuAD dataset as discussed in the paper.

With the advancements of Natural Language Understanding (NLU), diverse industrial applications like user intent classification, smart chatbots, sentiment analysis and question answering have be-come a primary paradigm. Transformers-based multi-lingual language models such as XLM have performed significantly well in diverse semantic understanding and classification tasks. However, fine-tuning such large pre-trained architectures is resource and compute intensive, limiting its wide adoption in enterprise environments.We present a novel efficient and light-weight frame-work based on sentence embeddings to obtain enhanced multi-lingual text representations for domain-specific NLU applications. Our framework combines the concepts of up-projection, alignment and meta-embeddings enhancing the textual semantic similarity knowledge of smaller sentence embedding architectures. Extensive experiments on diverse cross-lingual classification tasks showcase the proposed framework to be comparable to state-of-the-art large language models (in mono-lingual and zero-shot settings), even with lesser training and resource requirements.

Natural Language Understanding (NLU) has become a primary paradigm in enterprise settings for myriad industrial applications like user intent classification, smarter chatbots, sentiment analysis, and duplicate detection to name a few. With the advent of globalization, significant advancements have been recently achieved in transformers-based multi-lingual language models such as XLM and its variants for downstream multi-lingual sentence or short text classification tasks. However, fine-tuning such large pre-trained language models is highly resource-intensive as it assumes the adaptation of the full model, hampering its wide adoption in production grade applications due to the demanding computational and memory requirements.In this paper, we present a practical and efficient framework based on fusing various pre-trained sentence encoders leveraging the multi-lingual knowledge distillation approach. We demon-strate, for the first time, the practicality of utilizing such multi-lingual sentence embeddings for supervised learning tasks with a focus on sentence classification scenarios. We experimented our proposed framework on a wide range of open source classification datasets and exhibit very competitive performance compared to fine-tuning large pre-trained language models. We showcase that our light-weight framework provides the advantage of ease of training within minutes on a single CPU, competitive inference time, and robustness to parameter settings. In hope of facilitating and democratizing practical research focused on NLP, we are planning to release our code as well as a new pre-trained sentence embeddings for XLM-R-large model.

Sociological theories of career success provide fundamental principles for the analysis of social links to identify patterns that facilitate career development. Some theories (e.g. Granovetter's Strength of Weak Ties Theory and Burt's Structural Hole Theory) have shown that certain types of social ties provide career advantage to individuals by facilitating them to access unique information and connecting them with a diverse range of others in different social cliques. The assessment of link types and prediction of new links in the external social networks such as Facebook and Twitter have been studied extensively. However, this has not been addressed in the enterprise social networks and especially the prediction of weak ties in the context of employee career development. In this paper, we address this problem by proposing an Enterprise Weak Ties Recommendation (EWTR) framework which leverages enterprise social networks, employee collaboration activity streams and the organizational chart. We formulate weak ties recommendation as a link prediction problem. However, unlike any generic link prediction work, we first validated explicit enterprise social network with a set of heterogeneous collaboration networks and show assessment improves the explicit network's effectiveness in predicting new links. Furthermore, we leverage assessed social network for the weak ties prediction by optimizing the link prediction methods using organizational chart information. We demonstrate that optimization improves prediction accuracy in terms of AUC and average precision and our characterization of weak ties to a certain extent aligns with Granovetter's and Burt's seminal studies.

Sociological theories of career success provide fundamental principles for the analysis of social networks to identify patterns that facilitate career development. Structural Hole Theory argues that certain network structures provide advantages to individuals by facilitating them to access unique information from parts of the network. The network structural advantages of social networks in workplace settings have not been studied enough for the purpose of employees career development. In this paper, we address this challenge by proposing a Social Capital-Driven Career Development framework which leverages enterprise collaboration activity streams to assess employees social capital across organizational hierarchy levels. We demonstrate that our framework can enable employees to reflect on their social network structure from the prospective of information benefits for progressing their career from one hierarchy level to the immediate next level in their respective business units.

During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users’ choices. Recognizing crowd-mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this article, we propose a framework for Recognizing the Crowd Mobility Patterns in Cities using LBSN data. Our proposed framework comprises four main components: data gathering, recurrent crowd-mobility patterns extraction, temporal functional regions detection, and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization and Gaussian Kernel Density Estimation for extracting the recurrent crowd-mobility patterns in cities illustrating how crowd shifts from one area to another during each day across various time slots. Moreover, the framework employs a hierarchical clustering-based algorithm for identifying what we refer to as temporal functional regions by modeling functional areas taking into account temporal variation by means of check-ins’ categories. We build the framework using a spatial-temporal dataset crawled from Twitter for two entire years (2013 and 2014) for the area of Manhattan in New York City. We perform a detailed analysis of the extracted crowd patterns with an exploratory visualization showing that our proposed approach can identify clearly obvious mobility patterns that recur over time and location in the urban scenario. Using same time interval, we show that correlating the temporal functional regions with the recognized recurrent crowd-mobility patterns can yield to a deeper understanding of city dynamics and the motivation behind the crowd mobility. We are confident that our proposed framework not only can help in managing complex city environments and better allocation of resources based on the expected crowd mobility and temporal functional regions but also can have a direct implication on a variety of applications such as personalized recommender systems, anomalous event detection, disaster resilience management systems, and others.