Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Visual Question Answering: A Survey of Methods and Datasets

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.Comment: 25 page

DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Using the appraisal theory to analyze online restaurant reviews in the Lisbon region

Mestrado em Ciências EmpresariaisUser-generated content on Web 2.0 touristic websites can be important both for clients and companies of the sector. In the present work it were analyzed 503 online reviews, divided into 2769 sentence segments, from 22 restaurants in the Lisbon region, during the summer period 2012, on Tripadvisor.com. Resorting to an exploratory approach, the objective of this work is to identify the presence of attitude (affect, appreciation and judgement), in accordance with the Appraisal Theory. As well as verify the most mentioned attributes and polarity in each segment. Inter-rater agreement between two other evaluators was also checked, for attitude. The results obtained indicate that the dominant attitude is appreciation with positive polarity. Judgement is usually present when the service and Staff are mentioned, while affect is not often cited in this sample. This indicates that users tend to be more objective and less emotional on their restaurants evaluation. The most mentioned attributes were Quality of Food, Staff and Communication and Price, the majority of these had also positive polarity, which indicates that even in times of economic contention, Quality of Food should be the main focus. The inter-rater agreement was satisfactory. It is also concluded that user-generated content can be useful for managers to better understand the motivations, necessities and expectations of their clients and, in that way, focus their communication campaigns, products and services in order to answer these issues.A informação gerada pelos utilizadores em sítios turísticos Web 2.0 pode ser potencialmente importante tanto para clientes como para as empresas do sector. Neste trabalho foram analisados 503 comentários online, posteriormente divididos em 2769 segmentos de frase, provenientes do site Tripadisor.com referentes a 22 restaurantes da região de Lisboa, durante o período Verão de 2012. O objetivo do trabalho passou por, recorrendo a uma abordagem exploratória, identificar os tipos de atitude presente (afeto, apreciação e julgamento), de acordo com a Teoria da Avaliação. Assim como, verificar os tipos de atributos mais mencionados e a polaridade em cada segmento. Aferiu-se ainda a concordância da avaliação de atitude, recorrendo a dois avaliadores externos. Os resultados obtidos indicam que a atitude dominante é a apreciação com polaridade positiva. Julgamento é mencionado geralmente quando se aborda o serviço e o Staff, enquanto afeto foi pouco citado nesta amostra. Isto indica que os utilizadores tendem a ser mais objetivos e menos emocionais nas suas avaliações a restaurantes. Os atributos mais mencionados são Qualidade da Comida, Staff e Comunicação e Preço, todos com maioritariamente avaliação positiva, o que demonstra que mesmo em tempos de contenção a Qualidade da Comida deve continuar a ser a principal aposta. A concordância obtida foi satisfatória. Conclui-se ainda que a análise detalhada de comentários pode ser útil para que os gestores compreendam melhor as motivações, necessidades e expectativas dos seus clientes e dessa forma orientem as suas campanhas de comunicação e os seus produtos e serviços de forma a dar-lhes resposta

Alloprof: a new French question-answer education dataset and its use in an information retrieval case study

Teachers and students are increasingly relying on online learning resources to supplement the ones provided in school. This increase in the breadth and depth of available resources is a great thing for students, but only provided they are able to find answers to their queries. Question-answering and information retrieval systems have benefited from public datasets to train and evaluate their algorithms, but most of these datasets have been in English text written by and for adults. We introduce a new public French question-answering dataset collected from Alloprof, a Quebec-based primary and high-school help website, containing 29 349 questions and their explanations in a variety of school subjects from 10 368 students, with more than half of the explanations containing links to other questions or some of the 2 596 reference pages on the website. We also present a case study of this dataset in an information retrieval task. This dataset was collected on the Alloprof public forum, with all questions verified for their appropriateness and the explanations verified both for their appropriateness and their relevance to the question. To predict relevant documents, architectures using pre-trained BERT models were fine-tuned and evaluated. This dataset will allow researchers to develop question-answering, information retrieval and other algorithms specifically for the French speaking education context. Furthermore, the range of language proficiency, images, mathematical symbols and spelling mistakes will necessitate algorithms based on a multimodal comprehension. The case study we present as a baseline shows an approach that relies on recent techniques provides an acceptable performance level, but more work is necessary before it can reliably be used and trusted in a production setting

Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Akadēmiskā personāla angļu valodas komunikatīvās kompetences modelis

Promocijas darba „Akadēmiskā personāla angļu valodas komunikatīvās kompetences modelis” aktualitāti nosaka nepieciešamība augstskolām nodrošināt studiju procesa internacionalizāciju, kas nav iespējams bez akadēmiskā personāla angļu valodas zināšanām un prasmēm pasniegšanai multikulturālā vidē. Darbā ir apkopotas teorētiskās atziņas un veikts empīrisks pētījums par mutvārdu akadēmisko diskursu, lekcijas žanru, komunikatīvo valodas kompetenci, izmantojot kvalitatīvas un kvantitatīvas pētniecības metodes. Darbā tiek secināts, ka spēja lasīt veiksmīgas lekcijas angļu valodā ārvalstu studentiem lielā mērā ir atkarīga no akadēmiskā personāla angļu valodas komunikatīvās valodas kompetences, kas ietver visas sešas komunikatīvās valodas kompetences sastāvdaļas, kuras apskatītas šajā promocijas darbā. Atslēgvārdi: augstākā izglītība, lekcijas žanrs, mutvārdu akadēmiskais diskurss, angļu valodas komunikatīvās kompetences modelisThe novelty of the Doctoral thesis „Communicative English Language Competency Framework for the Academic Personnel” stems from the necessity of the academic personnel to ensure the internationalisation of the study process in the academia, which is impossible without the appropriate level of English language competency and skills necessary for working in the intercultural environment. The author summarized the theoretical findings and conducted an empirical research on spoken academic discourse, genre of an academic lecture, communicative language competences, using quantitative and qualitative research methods. It was concluded that a content lecture in English targeting the intentional audience is realized successfully when all six components of the communicative English language competence, described by the author, are coherent and are activated by the academic personnel. Keywords: tertiary education, lecture genre, spoken academic discourse, communicative English language competency framewor

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies