Namboodiri Indian Institute of Technology, Kanpur { badri,sandepkr,vinodkk,vinaypn} @iitk.ac.in Abstract Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr). 1 Introduction To understand the progress towards multimedia vision and language understanding, a visual Turing test was proposed by (Geman et al., 2015) that was aimed at visual question answering (Antol et al., 2015). Visual Dialog (Das et al., 2017) is a natural extension for VQA. Current dialog systems as evaluated in (Chattopadhyay et al., 2017) show that when trained between bots, AIAI dialog systems show improvement, but that does not translate to actual improvement for Human-AI dialog. This is because, the questions generated by bots are not natural (humanlike) and therefore does not translate to improved human dialog. Therefore it is imperative that improvement in the quality of questions will enable dialog agents to perform well in human interactions. Further, (Ganju et al., 2017) show that unanswered questions can be used for improving VQA, Image captioning and Object Classification. An interesting line of work in this respect is the work of (Mostafazadeh et al., 2016). Here the authors have proposed the challenging task of generating natural questions for an image. One aspect that is central to a question is the context that is relevant to generate it. As can be seen in Figure 1, an image with a person on a skateboard would result in questions related to the event.

Although there's quite some research done on irony detection, there's still not enough data generated to use for the actual training. The solution can be a large-scale irony dataset, which will allow to create complex training models and provide more accurate sentiment analysis. In order to address the lack of irony data the study analyzes more than 2 million tweets and proposes a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way. This is the first research that generates ironic sentences to achieve a high irony accuracy with well-preserved sentiment and content.

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. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social media and have pressured congress to restrict political advertising on social media. Following the related legislation, social media companies began disclosing their political ads archive for transparency during the summer of 2018 when the midterm election campaign was just beginning. This research collects the data of the related political ads in the context of the U.S. midterm elections since August to study the overall pattern of political ads on social media and uses sets of machine learning methods to conduct sentiment analysis on these ads to classify the negative ads. A novel approach is applied that uses AI image recognition to study the image data. Through data visualization, this research shows that negative advertising is still the minority, Republican advertisers and third party organizations are more likely to engage in negative advertising than their counterparts. Based on ordinal regressions, this study finds that anger evoked information-seeking is one of the main mechanisms causing negative ads to be more engaging and effective rather than the negative bias theory. Overall, this study provides a unique understanding of political advertising on social media by applying innovative data science methods. Further studies can extend the findings, methods, and datasets in this study, and several suggestions are given for future research.

Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora. To make the summarization results more faithful, this paper presents an unsupervised approach that combines rhetorical structure theory, deep neural model and domain knowledge concern for ATS. This architecture mainly contains three components: domain knowledge base construction based on representation learning, attentional encoder-decoder model for rhetorical parsing and subroutine-based model for text summarization. Domain knowledge can be effectively used for unsupervised rhetorical parsing thus rhetorical structure trees for each document can be derived. In the unsupervised rhetorical parsing module, the idea of translation was adopted to alleviate the problem of data scarcity. The subroutine-based summarization model purely depends on the derived rhetorical structure trees and can generate content-balanced results. To evaluate the summary results without golden standard, we proposed an unsupervised evaluation metric, whose hyper-parameters were tuned by supervised learning. Experimental results show that, on a large-scale Chinese dataset, our proposed approach can obtain comparable performances compared with existing methods.

Supervised topic models are often sought to balance prediction quality and in-terpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.

Dialog State Tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST usually fall into two categories, i.e, the picklist-based and span-based. From one hand, the picklist-based methods perform classifications for each slot over a candidate-value list, under the condition that a pre-defined ontology is accessible. However, it is impractical in industry since it is hard to get full access to the ontology. On the other hand, the span-based methods track values for each slot through finding text spans in the dialog context. However, due to the diversity of value descriptions, it is hard to find a particular string in the dialog context. To mitigate these issues, this paper proposes a Dual Strategy for DST (DS-DST) to borrow advantages from both the picklist-based and span-based methods, by classifying over a picklist or finding values from a slot span. Empirical results show that DS-DST achieves the state-of-the-art scores in terms of joint accuracy, i.e., 51.2% on the MultiWOZ 2.1 dataset, and 53.3% when the full ontology is accessible.

I was given huge autonomy over my project -- which revolved around NLP and sentiment analysis -- and was treated like any other full time employee. Ushur was a perfect place to learn, grow, and jumpstart my career in Silicon Valley.

Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such as BERT and GPT -2 (Devlin et al., 2019; Radford et al., 2019) have suggested the effectiveness of incorporating language priors in downstream NLP tasks. However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of the large pre-trained language model. It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations. ARDM outperforms or is on par with state-of-the-art methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is capable of generating humanlike responses to persuade people to donate to a charity. It has been a longstanding ambition for artificial intelligence researchers to create an intelligent conversational agent that can generate humanlike responses. Recently data-driven dialog models are more and more popular. However, most current state-of-the-art approaches still rely heavily on extensive annotations such as belief states and dialog acts (Lei et al., 2018). However, dialog content can vary considerably in different dialog tasks. Having a different intent or dialog act annotation scheme for each task is costly. For some tasks, it is even impossible, such as open-domain social chat. Thus, it is difficult to utilize these methods on challenging dialog tasks, such as persuasion and negotiation, where dialog states and acts are difficult to annotate.

Sometimes it happens that brands need to have a sentiment analysis. Knowing how people talk about your brand is essential. What do your community members think about your company? Do they praise you or do they mock of you? Are they sincerely impressed or that enthusiasm hides a sarcastic and brutal critic?