Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.Comment: EMNLP'22 Main Conferenc

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.Comment: EMNLP 202

Kinematics analysis and optimization of the exoskeleton’s knee joint

Two major defects of the exoskeleton’s single-axis knee joint were exposed in human-machine coordination experiments, which are chattering of hip and knee joints and pull-feeling at ankle joint. In order to analyze and solve these issues, human gait experiments were conducted to obtain the human gait data, and a kinematic model of the exoskeleton was established. Kinematics analysis of the exoskeleton based on the human’s hip and knee joint angles indicated the obvious human-machine ankle joint movement error; inverse kinematics analysis of the exoskeleton according to the human ankle joint trajectory reflected the abrupt angle changes of exoskeleton’s hip and knee joints. According to these analysis results, kinematics differences between the exoskeleton’s single-axis knee joint and human’s trochlea knee joint were regarded as the primary cause of the defects observed in human-machine coordination experiments. The exoskeleton’s knee joint was optimized in four-bar linkage type to imitate the kinematics characteristics of human’s knee joint. Kinematics simulation results of the optimized exoskeleton showed that human-machine ankle joint movement error and abrupt angle changes of the exoskeleton’s hip and knee joints have been both significantly reduced, thus the effectiveness of the exoskeleton’s knee joint optimization for improving the human-machine coordination could be confirmed

Trajectory tracking control of a hydraulic-tendon actuator with an application to the exoskeleton

This paper presents a hydraulic actuator and tendon drive system that was specifically designed for a lower-limb exoskeleton to provide high power and low inertia. The dynamics of the actuator-tendon system were analyzed based on the exoskeleton system and an adaptive sliding-mode trajectory tracking controller was designed for the drive system. The stability proof indicates that the controller is globally stable. The experimental results demonstrated that the controller provides high tracking accuracy and is robust to external disturbances and unmodeled nonlinearities. Moreover, the controller has less errors than the conventional PID controller. Further tests that included the joints of the exoskeleton were conducted to verify the performance of the controller

Inverse Kinematic Analysis and Evaluation of a Robot for Nondestructive Testing Application

The robot system has been utilized in the nondestructive testing field in recent years. However, only a few studies have focused on the application of ultrasonic testing for complex work pieces with the robot system. The inverse kinematics problem of the 6-DOF robot should be resolved before the ultrasonic testing task. A new effective solution for curved-surface scanning with a 6-DOF robot system is proposed in this study. A new arm-wrist separateness method is adopted to solve the inverse problem of the robot system. Eight solutions of the joint angles can be acquired with the proposed inverse kinematics method. The shortest distance rule is adopted to optimize the inverse kinematics solutions. The best joint-angle solution is identified. Furthermore, a 3D-application software is developed to simulate ultrasonic trajectory planning for complex-shape work pieces with a 6-DOF robot. Finally, the validity of the scanning method is verified based on the C-scan results of a work piece with a curved surface. The developed robot ultrasonic testing system is validated. The proposed method provides an effective solution to this problem and would greatly benefit the development of industrial nondestructive testing

Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label

Scribble-based weakly-supervised semantic segmentation using sparse scribble supervision is gaining traction as it reduces annotation costs when compared to fully annotated alternatives. Existing methods primarily generate pseudo-labels by diffusing labeled pixels to unlabeled ones with local cues for supervision. However, this diffusion process fails to exploit global semantics and class-specific cues, which are important for semantic segmentation. In this study, we propose a class-driven scribble promotion network, which utilizes both scribble annotations and pseudo-labels informed by image-level classes and global semantics for supervision. Directly adopting pseudo-labels might misguide the segmentation model, thus we design a localization rectification module to correct foreground representations in the feature space. To further combine the advantages of both supervisions, we also introduce a distance entropy loss for uncertainty reduction, which adapts per-pixel confidence weights according to the reliable region determined by the scribble and pseudo-label's boundary. Experiments on the ScribbleSup dataset with different qualities of scribble annotations outperform all the previous methods, demonstrating the superiority and robustness of our method.The code is available at https://github.com/Zxl19990529/Class-driven-Scribble-Promotion-Network

Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting

News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we study to which degree media balances news reporting and affects consumers through event inclusion or omission. We first introduce the task of detecting both partisan and counter-partisan events: events that support or oppose the author's political ideology. To conduct our study, we annotate a high-quality dataset, PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. We benchmark PAC to highlight the challenges of this task. Our findings highlight both the ways in which the news subtly shapes opinion and the need for large language models that better understand events within a broader context. Our dataset can be found at https://github.com/launchnlp/Partisan-Event-Dataset.Comment: EMNLP'23 Finding