In such systems the dialogue state tracker (DST) is a core component, aimed to maintain a distribution over the dialogue states based on the dialogue history. A dialogue state at any turn t in the dialogue is typically represented as a set of slot-value pairs, such as ( price, moderate) or ( food, italian) in the context of restaurant reservation. The goal of the DST is to determine the user's intent and the user's goal during the dialogue and represent them as such slot-value pairs. The downstream components of a dialogue system (e.g the dialogue manager) that are responsible to choose the next system action, rely on an accurate DST for an effective dialogue strategy. Because of the importance of DST in dialogue systems, their development attracted lots of research both in academia and industry. Typical dialogue systems are modeled for a fixed ontology consisting of a single domain (Mrk ˇ si c et al. 2017; Zhong, Xiong, and Socher 2018; Ren et al. 2018), and the domain ontology schema defines intents, slots and values for each slot of the domain.

Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.

Conversational AI is becoming an integral part of business practice across industries. More companies are adopting the advantages chatbots bring to customer service, sales, and marketing. Even though chatbots are becoming a "must-have" asset for leading businesses, their performance is still very far from human. Researchers from major research institutions and tech leaders have explored ways to boost the performance of dialog systems by increasing the diversity of their responses, enabling emotion recognition, improving their ability to track long-term aspects of the conversation, ensuring the maintenance of a consistent persona, etc. We've searched through important conversational AI research papers published in 2019 to present you the top 10 that set the new state-of-the-art in both task-oriented and open-domain dialog systems. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.

As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture, from standard architectures to architectures with jointly-trained components, single- or multi-party interactions, and offline or online training of any conversational agent component. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.

How do you test an application which constantly listens to the customers, learns their behaviour and create personalised engagements based out of learnings!! Today data plays a vital role in every decision making and hence making sense of the data to derive useful insights for our customers is a key for success. Sentiment Analysis is the process of classifying the data into positive, negative or neutral implemented using natural language processing (NLP) and Machine Learning techniques that helps in analysing the data to gauge public opinion, market research, monitor brand and product reputation, and understand customer experiences and is mostly offered as Sentiment Analysis as-a-Service . In this talk we will discuss the Challenges are around analysing, explicit and implict opinions, sarcasm, comparative opinions, Multilingual, Emojis, defination on neutral to just name a few and the strategies to test such applications with a use case on Airlines Sentiment (trained with tweets about airlines to identify between positive, neutral, and negative tweets).

Sentiment is a huge driving factor in the cryptocurrency market. But it is a metric which is very hard to measure. Sentiment analysis has been on the rise for the past few years. With the introduction of new packages, sentiment analysis can be done more quickly and efficiently than ever. In this post, you'll see why looking at the mood on the social media is not a great idea for sentiment analysis.

What does Minority Report, Black Mirror, and 1984 all have in common? Well, turn up to the talk to find out. On a day to day basis we countlessly write notes, send messages and respond to emails. The question is: what does what we write actually show about us, and how can we use the meaning behind these pieces of text to predict crimes and attacks. This talk delves into just this - how machine learning, and specifically natural language processing and sentiment analysis, can be used to predict crime and security attacks.

In this particular case, in only 10 seconds and with the small dataset provided, the CLI tool was able to run quite a few iterations, meaning training multiple times based on different combinations of algorithms/configuration with different internal data transformations and algorithm's hyper-parameters. Finally, the "best quality" model found in 10 seconds is a model using a particular trainer/algorithm with any specific configuration. Depending on the exploration time, the command can produce a different result. The selection is based on the multiple metrics shown, such as Accuracy. The first and easiest metric to evaluate a binary-classification model is the accuracy, which is simple to understand. "Accuracy is the proportion of correct predictions with a test data set.".

The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational Expectation-Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.

This edition of the conference on'Financial Evolution AI, Machine Learning & Sentiment Analysis' by UNICOM Seminars interrogates and explores the implications of AI & ML in the financial services industry. Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results.