For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL.To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs.Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.

We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field.

We live in a world where the globe is connected and communication made easy with the click of a button. In this social age, the evolution of the World Wide Web (www), characterized by the advancement of smartphones and semantic technology, has redefined social media as a retail platform and an indispensable marketing tool. Social media marketing (SMM) is a form of digital marketing that involves sharing content on social media platforms in an attempt to actualize a firm's branding, sales, and web traffic goals. Social media has become essential to helping brands connect to a wider range of customers, establish brand presence, and increase sales both in-store and online. Considering the size of this new virtual market, it comes as no surprise that marketers would choose to use social media to increase brand awareness.

Short text is a popular avenue of sharing feedback, opinions and reviews on social media, e-commerce platforms, etc. Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users' behaviour. However, obtaining high quality sentiment-associated and human interpretable themes still remains a challenge for short texts. In this paper we develop ELJST, an embedding enhanced generative joint sentiment-topic model that can discover more coherent and diverse topics from short texts. It uses Markov Random Field Regularizer that can be seen as a generalisation of skip-gram based models. Further, it can leverage higher-order semantic information appearing in word embedding, such as self-attention weights in graphical models. Our results show an average improvement of 10% in topic coherence and 5% in topic diversification over baselines. Finally, ELJST helps understand users' behaviour at more granular levels which can be explained. All these can bring significant values to the service and healthcare industries often dealing with customers.

The main motive of using sentiment analysis is to find out the true feelings of the varied people living in our society. It can be used for analyzing the customer feedback of a particular company, normal users on social media towards a product, services, social issues, or political agendas. Companies also use it for brand analysis, reputation crises, campaigns performances, competitor analysis, and improve the service offered to the customers. Analyzing the sentiments of the customers helps the customer support team to prioritize their work for offering better service to end-users. What are the common challenges with which sentiment analysis deals?

Capturing IT effort that is overlooked or misinterpreted by Key Performance Indicators. KPIs such as call duration are not necessarily the best way to measure the effectiveness your IT support staff. For example, a long phone call may mean that your agent is handling a complex issue--not having trouble resolving it. You can use Sentiment Analysis to identify the agents that are consistently involved in calls with a positive sentiment, so you can reward them and use them to mentor less experienced team members. By pulling sentiment data into your IT department's KPI reports, you can find correlations that might otherwise be hidden.

Though Arabic NLP has many challenges, it has seen many successes and developments.

In our current day and age, reviews are part of almost every product/service provided on the internet[14], as seen in [8] it is the primary way for a company to get an understanding concerning the amount of success their product has and as examined in [7] for the customer to build trust in purchasing or using a service of which only a description or a picture exits. Therefore, a need for a deeper understanding and analysis of those reviews are needed[9] for any individual who wishes to derive various consequences regarding a product. Standard methods for such insight derivation include sentiment analysis, around which we will formulate a new approach for review rating classification. Reviews across the internet mainly consist of text-based and rating-based formats, where in many cases, a combination of both is considered a single review; the method developed in this paper focuses on the ability to associate a review to a rating cluster based on sentiment proportions. We will define two main groups: one group consisting of a majority of reviews higher than three stars (in a 5-star ranking system) and another group of all reviews, which correspond to the less than three stars.

We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

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