Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowle
dge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.
News recommendation techniques can help users on news platforms obtain their preferred news information. Most existing news recommendation methods rely on centrally stored user behavior data to train models and serve users. However, user data is usua
lly highly privacy-sensitive, and centrally storing them in the news platform may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains a stage for candidate news generation (i.e., recall) and a stage for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.
Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be prof
ound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP. In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection. Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language. We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research. Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit. We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.
We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, Germ
an, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.
Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses
a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics.
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
f 12 are followed by Kaggle-style coding assigments. Our course intents to serve multiple purposes: (i) familirize students with the core concepts and methods in NLP, such as language modelling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are build upon these concepts; (iii) to introduce architectures for most most demanded real-life applications, (iii) to develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020 and so far have received positive feedback.
Natural Language Processing offers new insights into language data across almost all disciplines and domains, and allows us to corroborate and/or challenge existing knowledge. The primary hurdles to widening participation in and use of these new rese
arch tools are, first, a lack of coding skills in students across K-16, and in the population at large, and second, a lack of knowledge of how NLP-methods can be used to answer questions of disciplinary interest outside of linguistics and/or computer science. To broaden participation in NLP and improve NLP-literacy, we introduced a new tool web-based tool called Natural Language Processing 4 All (NLP4All). The intended purpose of NLP4All is to help teachers facilitate learning with and about NLP, by providing easy-to-use interfaces to NLP-methods, data, and analyses, making it possible for non- and novice-programmers to learn NLP concepts interactively.
Advertisement of medical products on social media has become increasingly common, and is associated with increased online shopping in pursuit of self-medication. Such practice highlights the influence of social media advertising on individual use of
medicinal products without consultation with health care professionals.
Objectives: To better understand the practice of online self-medication and investigate its prevalence in Palestine, this study specifically assessed the probable reasons, extent of use, and source of advice for online self-medication among university students in Palestine. In addition, the study evaluated factors that influence online self-medication in this population, such as gender, age, knowledge in medical specialty, and perception of online products.
Methods: This study was conducted using a "paper pretested questionnaire" prepared in the Arabic language and self-administered to 700 students from three public universities in Palestine (Al-Najah, Al-Quds, and Bethlehem Universities). The study was conducted over three months (Nov. 2019 – Jan. 2020) and included university students of all years from both medical and nonmedical faculties. Data were collected, coded, entered, analyzed, and summarized using Statistical Package for Social Sciences version 25. Descriptive results were expressed as frequency, percentage, and mean±SD.
Results: Female (87.6%), younger (20-29 years), and medical (57.4%) students tended to use online self-medication more than their peers. Respondents practiced online self-medication to save time (50.4%) and money (49.8%), and a majority (65.7%) reported using online products without consulting physician or pharmacist.
Nearly a third of respondents (29.6%) reported that they did not have any instructions on how to use products, and a significant number experienced side effects from the products they used (p-value <0.001). The internet was the most commonly reported source for self-medication (45.3%), particularly sponsored advertising campaigns on websites (16.7%). In terms of product type, skin care products (76.7%) were the most commonly used, followed by hair products (72.2%), and vitamins (58.8%). In addition, cream and ointments were the most frequently used dosage forms (71.3%). The majority of respondents (64.1%) described their experience as “bad” and “not healthy”; half (50.9%) reported having side-effects and a third (33.6%) stopped using the products because of side effects. Statistical analysis showed that the difference in usage between genders was significant for vitamins, traditional herbs, weight loss products, hair products, skin products, nail products, and food supplements (p-value <0.05). In addition, the relation between specialty domain and product use was significant for hair products, food supplements, traditional herbs, and herbal mixtures (p-value <0.05). The relationship between reading information and experiencing side effects was significant with a p-value of 0.000. Finally, the relationship between side effects and product re-use was significant (p-value <0.05)
Conclusions: Online self-medication is a common practice of young Palestinian university students; this constitutes a health problem, and intervention is needed to minimize risk. We emphasize the important role of health care professionals in educating the community, especially the youth (<30 years), regarding online medication practices that may have harmful side effects.
This study aimed to determine the role of direct marketing through its means (direct mail, internet, catalog, phone) in improving customer satisfaction at Tishreen University in Lattakia Governorate. The researcher followed the descriptive and analyt
ical approach in his study, and a set of methods, including relying on secondary and primary data through a questionnaire that was completed It was designed, and it was distributed to (179) respondents, of which (175) were retrieved, and (171) questionnaires were valid for analysis, and the research community was formed from clients of Tishreen University in Lattakia Governorate. The study concluded that there is a statistically significant relationship between Tools of direct marketing (direct mail, internet, catalog, phone) and customer satisfaction. The average of the responses of the sample members for the direct mail variable was 2.41, which indicates that the university does not send its service offers to clients via direct mail, and that the university does not send its offers to current clients only, and that the university does not formulate mail messages (marketing offers) in a personal capacity, and that the rates The response is not very high due to the use of direct mail.
With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communitie
s has been relatively limited. Moreover, as the underlying methodologies used in these studies mainly conform to the traditional machine learning models and statistical methods, the scope for introducing contextualized word representations for topic and theme extraction from online mental health communities remains open. Thus, in this research, we propose topic-infused deep contextualized representations, a novel data representation technique that uses autoencoders to combine deep contextual embeddings with topical information, generating robust representations for text clustering. Investigating the Reddit discourse on Post-Traumatic Stress Disorder (PTSD) and Complex Post-Traumatic Stress Disorder (C-PTSD), we elicit the thematic clusters representing the latent topics and themes discussed in the r/ptsd and r/CPTSD subreddits. Furthermore, we also present a qualitative analysis and characterization of each cluster, unraveling the prevalent discourse themes.
