The connector leverages eGain's unique BYOB (Bring Your Own Bot) architecture, allowing business users to easily plug in the Watson Assistant into the eGain platform with no coding. Per Gartner, less than 10% of customer service journeys are fulfilled using self-service, which is why it is critical to integrate chatbots with human-assisted service channels such as live chat. The eGain Connector for Watson Assistant improves customer, agent, and business experiences at once. When customers escalate from Watson to human-assisted chat, their context is passed to the contact center agent so that they do not need to repeat information to the agent. Agents get to see interactions that customers have already had with Watson before they start their conversation with the customer.

In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning and reasoning. The new emerging Neurosymbolic field is currently experiencing a renaissance, as novel frameworks and algorithms motivated by various practical applications are being introduced, building on top of the classical neural and reasoning problem setting. This article aims to provide a comprehensive review of significant recent developments in real-world applications of Neurosymbolic Artificial Intelligence. Specifically, we introduce a taxonomy of common Neurosymbolic applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.

Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or products. There is a ``cost of fairness'' borne by the consumer side of the interaction when such a solution is implemented. This consumer-side cost raises its own questions of fairness, particularly when personalization is used to control the impact of the fairness constraint. In adopting a personalized approach to the fairness objective, researchers may be opening their systems up to strategic behavior on the part of users. This type of incentive has been studied in the computational social choice literature under the terminology of ``bossiness''. The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others. This position paper introduces the concept of bossiness, shows its application in fairness-aware recommendation and discusses strategies for reducing this strategic incentive.

Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this work, we argue that popularity bias often leads to other biases that are not obvious when additional user or item information is not provided to the researcher. We examine our hypothesis in the book recommendation case on a commonly used dataset with book ratings. We enrich it with author information using publicly available external sources. We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles. We conclude that the societal implications of popularity bias should be further examined by the scholar community.

Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-of-words techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.

Voice AI is expected to reduce global call centre labour costs by as much as US$80 billion by 2026, according to Gartner. However, Australia's adoption of conversational AI for customer service remains relatively low compared to other countries. "Nine-in-ten Australians are now smartphone users, with Siri, Google or Alexa at their fingertips," said Kun Wu, Founder and Managing Director of AI Rudder, a leading voice AI provider in the Asian Pacific (APAC). Voice AI technologies have enhanced consumers' lives in terms of communication, media use, entertainment and information searches. Given this high consumer penetration, Wu says the potential exists for the Australian customer service industry to follow APAC markets in using voice AI to deliver high-quality service.

Healthcare leaders don't need reminding that they face serious nurse shortages. It's a challenge they have faced for years – and it's become a critical concern as record numbers leave their practices due to burnout and exhaustion. The November 2021 Hospital IQ Survey found that a full 90% of nurse respondents were considering resigning to seek new careers. Another disturbing result: 71% of nurses with 15 or more years of experience – an invaluable resource to health systems – said they were on the verge of leaving. The stakes to relieve the stress on nurses have never been higher.

Modernizing industries depend heavily on emerging technologies. These technologies, like artificial intelligence, are primarily impactful for the manufacturing, energy, and transportation sectors. Enterprises are being transformed into a digital environment with emerging technologies. Every time the phrase "technology" is used, something new is always being developed or put into use that could benefit organizations. A few years ago, no one thought emerging technologies would soon take over our lives. The users' quick development has impacted the business ecosystem in wants and expectations for real-time interaction on these applications.

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.

Conversational recommender systems (CRS) that are able to interact with users in natural language often utilize recommendation dialogs which were previously collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities that indicate the users' preferences. In order to precisely model the seekers' preferences and respond consistently, CRS typically rely on item and entity annotations. A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conversational recommendation, where items and entities were annotated using automatic keyword or pattern matching techniques. An analysis of this dataset unfortunately revealed that there is a substantial number of cases where items and entities were either wrongly annotated or annotations were missing at all. This leads to the question to what extent automatic techniques for annotations are effective. Moreover, it is important to study impact of annotation quality on the overall effectiveness of a CRS in terms of the quality of the system's responses. To study these aspects, we manually fixed the annotations in INSPIRED. We then evaluated the performance of several benchmark CRS using both versions of the dataset. Our analyses suggest that the improved version of the dataset, i.e., INSPIRED2, helped increase the performance of several benchmark CRS, emphasizing the importance of data quality both for end-to-end learning and retrieval-based approaches to conversational recommendation. We release our improved dataset (INSPIRED2) publicly at https://github.com/ahtsham58/INSPIRED2.