In this paper, we introduce the task of style-consistent content transfer, which concerns modifying a text’s content based on a provided reference statement while preserving its original style. We approach the task by employing multi-task learning to ensure that the modified text meets three important conditions: reference faithfulness, style adherence, and coherence. In particular, we train three independent classifiers for each condition. During inference, these classifiers are used to determine the best modified text variant. Our evaluation, conducted on hotel reviews and news articles, compares our approach with sequence-to-sequence and error correction baselines. The results demonstrate that our approach reasonably generates text satisfying all three conditions. In subsequent analyses, we highlight the strengths and limitations of our approach, providing valuable insights for future research directions.

When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments.Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments.However, existing approaches do not perform any audience-specific adaptation.In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs.To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs.Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs.In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief.Our results reveal the limitations of modeling users' beliefs based on their stances.Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page.Conventional snippets are extractive in nature, which recently gave rise to copyright claims from news publishers as well as a new copyright legislation being passed in the European Union, limiting the fair use of web page contents for snippets.At the same time, abstractive summarization has matured considerably in recent years, potentially allowing for more personalization of snippets in the future.Taken together, these facts render further research into generating abstractive snippets both timely and promising.

An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page. Compared to the conventional extractive snippets, which are generated by extracting phrases and sentences verbatim from a web page, abstractive snippets circumvent copyright issues; even more interesting is the fact that they open the door for personalization. Abstractive snippets have been evaluated as equally powerful in terms of user acceptance and expressiveness—but the key question remains: Can abstractive snippets be automatically generated with sufficient quality?

This paper introduces the task of "flipping" the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias.To study this task, we create a corpus with bias-labeled articles from allsides.com.As a first step, we analyze the corpus and discuss intrinsic characteristics of bias.They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process.The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article headlines.A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias.We apply an autoencoder incorporating information from an article's content to learn how to automatically flip the bias.From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias.Insights from our analysis shed light on how to solve the main challenges of bias flipping.

The snippets in the result list of a web search engine are built with sentences from the retrieved web pages that match the query. Reusing a web page's text for snippets has been considered fair use under the copyright laws of most jurisdictions. As of recent, notable exceptions from this arrangement include Germany and Spain, where news publishers are entitled to raise claims under a so-called ancillary copyright. A similar legislation is currently discussed at the European Commission. If this development gains momentum, the reuse of text for snippets will soon incur costs, which in turn will give rise to new solutions for generating truly original snippets. A key question in this regard is whether the users will accept any new approach for snippet generation, or whether they will prefer the current model of "reuse snippets." The paper in hand gives a first answer. A crowdsourcing experiment along with a statistical analysis reveals that our test users exert no significant preference for either kind of snippet. Notwithstanding the technological difficulty, this result opens the door to a new snippet synthesis paradigm.

Web post and user stance labeling is challenging not only because of the informality and variation in language on the Web but also because of the lack of labeled data on fast-emerging new topics-even the labeled data we do have are usually heavily skewed. In this paper, we propose a joint user-post approach for stance labeling to mitigate the latter two difficulties. In labeling post stance, the proposed approach considers post content as well as posting and liking behavior, which involves users. Sentiment analysis is applied to posts to acquire their initial stance, and then the post and user stance are updated iteratively with correlated posting-related actions. The whole process works with limited labeled data, which solves the first problem. We use real interaction between authors and readers for stance labeling. Experimental results show that the proposed approach not only substantially improves content-based post stance labeling, but also yields better performance for the minor stance class, which solves the second problem.

Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work, showing that UTCNN performs well regardless of language or platform.

Language learners' limited lexical knowledge leads to imprecise wording. This is especially true when they attempt to express their emotions. Many learners rely heavily on the traditional thesaurus. Unfortunately, this fails to provide appropriate suggestions for lexical choices. To better aid English-as-a-second-language learners with word choices, we propose RESOLVE, which provides ranked synonyms of emotion words based on contextual information. RESOLVE suggests precise emotion words regarding the events in the relevant context. Patterns are learned to capture emotion events, and various factors are considered in the scoring function for ranking emotion words. We also describe an online writing system developed using RESOLVE and evaluate its effectiveness for learning assistance with a writing task. Experimental results showed that RESOLVE yielded a superior performance on NDCG@5 which significantly outperformed both PMI and SVM approaches, and offered better suggestions than Roget's Thesaurus and PIGAI (an online automated essay scoring system). Moreover, when applying it to the writing task, students' appropriateness with emotion words was 30 percent improved. Less-proficient learners benefited more from RESOLVE than highly-proficient learners. Post-tests also showed that after using RESOLVE, less-proficient learners' ability to use emotion words approached that of highly-proficient learners. RESOLVE thus enables learners to use precise emotion words.