Yingjie Li


2023

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Distilling Calibrated Knowledge for Stance Detection
Yingjie Li | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL 2023

Stance detection aims to determine the position of an author toward a target and provides insights into people’s views on controversial topics such as marijuana legalization. Despite recent progress in this task, most existing approaches use hard labels (one-hot vectors) during training, which ignores meaningful signals among categories offered by soft labels. In this work, we explore knowledge distillation for stance detection and present a comprehensive analysis. Our contributions are: 1) we propose to use knowledge distillation over multiple generations in which a student is taken as a new teacher to transfer knowledge to a new fresh student; 2) we propose a novel dynamic temperature scaling for knowledge distillation to calibrate teacher predictions in each generation step. Extensive results on three stance detection datasets show that knowledge distillation benefits stance detection and a teacher is able to transfer knowledge to a student more smoothly via calibrated guiding signals. We publicly release our code to facilitate future research.

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Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction
Hao Li | Yanan Cao | Yubing Ren | Fang Fang | Lanxue Zhang | Yingjie Li | Shi Wang
Findings of the Association for Computational Linguistics: EMNLP 2023

Event argument extraction is critical to various natural language processing tasks for providing structured information. Existing works usually extract the event arguments one by one, and mostly neglect to build dependency information among event argument roles, especially from the perspective of event structure. Such an approach hinders the model from learning the interactions between different roles. In this paper, we raise our research question: How to adequately model dependencies between different roles for better performance? To this end, we propose an intra-event and inter-event dependency-aware graph network, which uses the event structure as the fundamental unit to construct dependencies between roles. Specifically, we first utilize the dense intra-event graph to construct role dependencies within events, and then construct dependencies between events by retrieving similar events of the current event through the retrieval module. To further optimize dependency information and event representation, we propose a dependency interaction module and two auxiliary tasks to improve the extraction ability of the model in different scenarios. Experimental results on the ACE05, RAMS, and WikiEvents datasets show the great advantages of our proposed approach.

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Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
Yun Luo | Zhen Yang | Fandong Meng | Yingjie Li | Jie Zhou | Yue Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. Previous research has demonstrated that contextual information is crucial for developing an effective ASE model. However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences. To tackle this challenge, we propose an Efficient Context-aware ASE model (ECASE) that fully exploits contextual information by enhancing modeling capacity and augmenting training data. Specifically, we introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Additionally, we augment the training data by randomly masking discourse markers and sentences, which reduces the model’s reliance on specific words or less informative sentences. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance. Furthermore, ablation studies confirm the effectiveness of each module in our model.

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A New Direction in Stance Detection: Target-Stance Extraction in the Wild
Yingjie Li | Krishna Garg | Cornelia Caragea
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Stance detection aims to detect the stance toward a corresponding target. Existing works use the assumption that the target is known in advance, which is often not the case in the wild. Given a text from social media platforms, the target information is often unknown due to implicit mentions in the source text and it is infeasible to have manual target annotations at a large scale. Therefore, in this paper, we propose a new task Target-Stance Extraction (TSE) that aims to extract the (target, stance) pair from the text. We benchmark the task by proposing a two-stage framework that first identifies the relevant target in the text and then detects the stance given the predicted target and text. Specifically, we first propose two different settings: Target Classification and Target Generation, to identify the potential target from a given text. Then we propose a multi-task approach that takes target prediction as the auxiliary task to detect the stance toward the predicted target. We evaluate the proposed framework on both in-target stance detection in which the test target is always seen in the training stage and zero-shot stance detection that needs to detect the stance for the targets that are unseen during the training phase. The new TSE task can facilitate future research in the field of stance detection.

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C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection
Chenye Zhao | Yingjie Li | Cornelia Caragea
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor of, against, or neutral toward a target that is unseen during training. Despite the growing attention on ZSSD, most recent advances in this task are limited to English and do not pay much attention to other languages such as Chinese. To support ZSSD research, in this paper, we present C-STANCE that, to our knowledge, is the first Chinese dataset for zero-shot stance detection. We introduce two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD. Our dataset includes both noun-phrase targets and claim targets, covering a wide range of domains. We provide a detailed description and analysis of our dataset. To establish results on C-STANCE, we report performance scores using state-of-the-art deep learning models. We publicly release our dataset and code to facilitate future research.

2021

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A Multi-Task Learning Framework for Multi-Target Stance Detection
Yingjie Li | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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P-Stance: A Large Dataset for Stance Detection in Political Domain
Yingjie Li | Tiberiu Sosea | Aditya Sawant | Ajith Jayaraman Nair | Diana Inkpen | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation
Yingjie Li | Chenye Zhao | Cornelia Caragea
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion. Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations. Besides, most previous works focused on a hard-label training in which meaningful similarities among categories are discarded during training. To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively. We show that models can learn more universal representations with respect to targets in these settings. Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings. Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions. Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin. We publicly release our code.

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Target-Aware Data Augmentation for Stance Detection
Yingjie Li | Cornelia Caragea
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.

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Stance Detection in COVID-19 Tweets
Kyle Glandt | Sarthak Khanal | Yingjie Li | Doina Caragea | Cornelia Caragea
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.

2019

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Multi-Task Stance Detection with Sentiment and Stance Lexicons
Yingjie Li | Cornelia Caragea
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Stance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention mechanism or sentiment information. In this paper, we propose a multi-task framework that incorporates target-specific attention mechanism and at the same time takes sentiment classification as an auxiliary task. Moreover, we used a sentiment lexicon and constructed a stance lexicon to provide guidance for the attention layer. Experimental results show that the proposed model significantly outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset.