Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text.Token-level diffusion doesn't model wordorder dependencies explicitly and operates on short, fixed output windows, while passagelevel diffusion struggles with learning robust representations for long-form text.To address these challenges, we propose Segment-Level Diffusion (SLD), a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance.By segmenting long-form outputs into multiple latent representations and decoding them with an autoregressive decoder, SLD simplifies diffusion predictions and improves scalability.Experiments on four datasets demonstrate that, when compared to other diffusion and autoregressive baselines SLD achieves competitive or superior fluency, coherence, and contextual compatibility in automatic and human evaluations.1 1 Our code is available at: https://github.com/ SpaceHunterInf/Segment_Level_Diffusion Encoder 𝑖 !𝑖 "

Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utterances. In 64% of these conversations, the group members are able to find a better solution than they had identified individually, and in 43.8% of the groups who had a correct answer as their final solution, none of the participants had solved the task correctly by themselves. Furthermore, we propose a novel annotation schema that captures deliberation cues and release all 14k utterances annotated with it. Finally, we use the proposed dataset to develop and evaluate two methods for generating deliberation utterances. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz.

People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels.By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision.In this work, we investigate methods for detecting what makes someone change their mind.To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task.To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection.Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a languageaware model with learning-to-rank training.Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction.The present level of proliferation of fake, biased, and propagandistic content online, has made it impossible to fact-check every single suspicious claim, either manually or automatically.Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.This approach makes it possible to detect likely "fake news" the moment they are published, by simply checking the reliability of their source.From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context.Here, we study the impact of both, namely (i) what was written (i.e., what was published by the target medium, and how it describes itself on Twitter) vs. (ii) who read it (i.e., analyzing the readers of the target medium on Facebook, Twitter, and YouTube).We further study (iii) what was written about the target medium on Wikipedia.The evaluation results show that what was written matters most, and that putting all information sources together yields huge improvements over the current state-of-the-art.

In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to move away from the "ten blue links" metaphor, as mobile users are less likely to click on them, expecting to get the answer directly from the snippets. In turn, this has revived the interest in Question Answering. Then, along came chatbots, conversational systems, and messaging platforms, where the user needs could be better served with the system asking follow-up questions in order to better understand the user's intent. While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent. However, this possibility should not be overused, as this practice could confuse and/or annoy the user. How to produce good variable-length lists, given the conflicting objectives of staying short while maximizing the likelihood of having a correct answer included in the list, is an underexplored problem. It is also unclear how to evaluate a system that tries to do that. Here we aim to bridge this gap. In particular, we define some necessary and some optional properties that an evaluation measure fit for this purpose should have. We further show that existing evaluation measures from the IR tradition are not entirely suitable for this setup, and we propose novel evaluation measures that address it satisfactorily.

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.

We present a study on predicting the factuality of reporting and bias of news media.While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media.These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems.We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts.The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.