Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models (PLMs), those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model can process several documents at once and generate composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives? For example, what drives people to say "grasp the meaning" rather than "understand the meaning" within a specific context? Many NLP approaches to metaphorical language rely on cognitive and (psycho-)linguistic insights and have successfully defined models of discourse coherence, abstractness and affect. In this work, we build five simple models relying on established cognitive and linguistic properties -- frequency, abstractness, affect, discourse coherence a…
Epistemic orientation in parliamentary discourse is associated with deliberative democracy
Department of Politics and Public Administration, University of Konstanz（康斯坦茨大学政治与公共行政系）；School of Psychological Science, University of Bristol（布里斯托大学心理学科学学院）；Complexity Science Hub（复杂性科学中心）；Department of Psychology, University of Potsdam（波茨坦大学心理学系）
Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans
incubator for Media Education and Development (iMEdD)（媒体教育与发展孵化器（iMEdD））；School of Political Sciences, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学政治科学学院）；School of Informatics, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学信息学院）
Recent works show that discourse analysis benefits from modeling intra- and inter-sentential levels separately, where proper representations for text units of different granularities are desired to capture both the meaning of text units and their relations to the context. In this paper, we propose to take advantage of transformers to encode contextualized representations of units of different levels to dynamically capture the information required for discourse dependency analysis on intra- and inter-sentential levels. Motivated by the observation of writing patterns commonly shared across arti…
ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Advanced Intelligence and Computing, A*STAR（新加坡科研局先进智能与计算研究所）
Quantifying the Uniqueness and Divisiveness of Presidential Discourse
Department of Computer Science, The University of Chicago（芝加哥大学计算机科学系）；Department of Political Science, The University of Chicago（芝加哥大学政治学系）；Harris School of Public Policy, The University of Chicago（芝加哥大学哈里斯公共政策学院）；Harvard University（哈佛大学）
Sony AI（索尼人工智能研究院）；Technical University of Munich（慕尼黑工业大学）；Princeton University（普林斯顿大学）
A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in a…
Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019). In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a). We also present a detailed analysis on why de…
We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical taxonomies. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We use our continuous representations to resolve queries with short latency using approximate nearest neighbor search on sentence level. We apply th…
ParityYEG；University of Alberta（阿尔伯塔大学）；Dev Edmonton Society（埃德蒙顿发展协会）；University of Toronto（多伦多大学）
Including diverse voices in political decision-making strengthens our democratic institutions. Within the Canadian political system, there is gender inequality across all levels of elected government. Online abuse, such as hateful tweets, leveled at women engaged in politics contributes to this inequity, particularly tweets focusing on their gender. In this paper, we present ParityBOT: a Twitter bot which counters abusive tweets aimed at women in politics by sending supportive tweets about influential female leaders and facts about women in public life. ParityBOT is the first artificial intell…
Tohoku University（东北大学）；Riken Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Stony Brook University（石溪大学）
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation. We introduce several types of token, sentence and paragraph-level corruption techniques fo…
The Hong Kong University of Science and Technology（香港科技大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level. Previous studies mainly focus on the interactions between two arguments. We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together. Extensive experim…
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural l…
The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spreading frames, little is known about how ordinary people on social media frame political issues. By creating a new dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory, we develop supervised models to detect frames. We demonstrate how users' ideology and region impact framing choices, and how a message's framing influences audience responses. We find that the more commonly-used issue-generic frames obscu…
Extracting narrative signals from public discourse: a network-based approach
Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克数学科学研究所）；Laboratoire Lattice, École Normale Supérieure - PSL - CNRS - Univ. Sorbonne Nouvelle（格勒诺布尔大学拉提实验室）；médialab, Sciences Po（社会科学高等学院媒体实验室）；Brussels School of Governance, Vrije Universiteit Brussel（布鲁塞尔治理学校，自由大学布鲁塞尔）；imec-SMIT, Vrije Universiteit Brussel（imec-SMIT，自由大学布鲁塞尔）
Booz Allen Hamilton（博思艾伦咨询公司）；International Water Management Institute（国际水管理研究院）；NOVA Information Management School (NOVA IMS)（NOVA信息管理学院）；New York University Abu Dhabi（纽约大学阿布扎比分校）
People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people's social, political and economic experiences of extreme weather events. In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.
