As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues. To combat this issue, we have introduced PerSphere, a benchmark designed to facilitate multi-faceted perspective retrieval and summarization, thus breaking free from these information silos. For each query within PerSphere, there are two opposing claims, each supported by distinct, non-overlapping perspectives drawn from one or more documents. Our goal is to accurately summarize these documents, aligning the summaries with the respective claims and their underlying perspectives. This task is structured as a two-step end-to-end pipeline that includes comprehensive document retrieval and multi-faceted summarization. Furthermore, we propose a set of metrics to evaluate the comprehensiveness of the retrieval and summarization content. Experimental results on various counterparts for the pipeline show that recent models struggle with such a complex task. Analysis shows that the main challenge lies in long context and perspective extraction, and we propose a simple but effective multi-agent summarization system, offering a promising solution to enhance performance on PerSphere.

Bowen Ding, Qingkai Min, Shengkun Ma, Yingjie Li, Linyi Yang, Yue Zhang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Stance detection aims to detect the stance toward a corresponding target.Existing works have achieved promising progress on stance detection tasks in which the goal is to predict the stance given both a target and a text.However, they all work under 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 multitask 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 unseen target during the inference stage.The new TSE task can facilitate future research in the field of stance detection.We publicly release our code.1

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.

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.

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). 2021.

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 hardlabel 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.1

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.

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). 2019.