pwc-archive/papers-with-abstracts huggingface dataset:
Transferring Confluent Knowledge to Argument Mining
Authors: João António Rodrigues, António Branco
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Argument Mining, Transfer Learning

Relevant to all application domains where it is important to get at the reasons underlying sentiments and decisions, argument mining seeks to obtain structured arguments from unstructured text and has been addressed by approaches typically involving some feature and/or neural architecture engineering. By adopting a transfer learning methodology, and by means of a systematic study with a wide range of knowledge sources promisingly suitable to leverage argument mining, the aim of this paper is to empirically assess the potential of transferring such knowledge learned with confluent tasks. By adopting a lean approach that dispenses with heavier feature and model engineering, this study permitted both to gain novel empirically based insights into the argument mining task and to establish new state of the art levels of performance for its three main sub-tasks, viz. identification of argument components, classification of the components, and determination of the relation among them.
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance
Authors: Longfeng Li, Haifeng Sun, Qi Qi, Jingyu Wang, Jing Wang, Jianxin Liao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to distinguish sentiment polarity of every specific aspect in a given sentence. Previous researches have realized the importance of interactive learning with context and aspects. However, these methods are ill-studied to learn complex sentence with multiple aspects due to overlapped polarity feature. And they do not consider the correlation between aspects to distinguish overlapped feature. In order to solve this problem, we propose a new method called Recurrent Inverse Learning Guided Network (RILGNet). Our RILGNet has two points to improve the modeling of aspect correlation and the selecting of aspect feature. First, we use Recurrent Mechanism to improve the joint representation of aspects, which enhances the aspect correlation modeling iteratively. Second, we propose Inverse Learning Guidance to improve the selection of aspect feature by considering aspect correlation, which provides more useful information to determine polarity. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of RILGNet, and we further prove that RILGNet is state-of-the-art method in multiaspect scenarios.
Analyzing Persuasion Strategies of Debaters on Social Media
Authors: Matti Wiegmann, Khalid Al Khatib, Vishal Khanna, Benno Stein
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Persuasion Strategies, Text Generation

Existing studies on the analysis of persuasion in online discussions focus on investigating the effectiveness of comments in discussions and ignore the analysis of the effectiveness of debaters over multiple discussions. In this paper, we propose to quantify debaters effectiveness in the online discussion platform: “ChangeMyView” in order to explore diverse insights into their persuasion strategies. In particular, targeting debaters with different levels of effectiveness (e.g., good vs. bad), various behavioral characteristics (e..g, engagement) and text stylistic features (e.g., used frames) of debaters are carefully examined, leading to several outcomes that can be the backbone of writing assistants and persuasive text generation.
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
Authors: Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, Yifei Zhang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Common Sense Reasoning, Sentiment Analysis

Sentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there are sentiment words in language expression. There have been many research results in explicit sentiment analysis. However, implicit sentiment analysis is rarely studied. Compared with explicit sentiment expression, implicit sentiment expression usually omits a lot of knowledge and common sense, and context also has an important impact on implicit sentiment expression. In this paper, we use a knowledge graph to supplement implicit sentiment expression and propose a novel Implicit Sentiment Analysis model combining Knowledge enhancement and Context features (dubbed KC-ISA). The KC-ISA model can effectively integrate external knowledge and contextual features by the coattention mechanism. Finally, we conduct experiments on the SMP2019 implicit sentiment analysis dataset. Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets. The results on three datasets show that our proposed KC-ISA model can achieve better results on text sentiment analysis.
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning
Authors: Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Contrastive Learning, Cross-Domain Text Classification, Domain Adaptation, Domain Generalization, Rumour Detection, text-classification, Text Classification

While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation. In this paper, we tackle the more challenging task of domain generalization, in which domain-invariant representations are learned from multiple source domains, without access to any data from the target domains, and classification decisions are then made on test documents in unseen target domains. We propose a novel framework based on supervised contrastive learning with a memory-saving queue. In this way, we explicitly encourage examples of the same class to be closer and examples of different classes to be further apart in the embedding space. We have conducted extensive experiments on two Amazon review sentiment datasets, and one rumour detection dataset. Experimental results show that our domain generalization method consistently outperforms state-of-the-art domain adaptation methods.
A Zero-Shot Claim Detection Framework Using Question Answering
Authors: Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Misinformation, Object, object-detection, Object Detection, Question Answering

In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection. This involves detecting more fine-grained attributes relating to the claim, such as the claimer, claim topic, claim object pertaining to the topic, etc. Yet, a notable bottleneck of existing claim detection approaches is their portability to emerging events and low-resource training data settings. In this regard, we propose a fine-grained claim detection framework that leverages zero-shot Question Answering (QA) using directed questions to solve a diverse set of sub-tasks such as topic filtering, claim object detection, and claimer detection. We show that our approach significantly outperforms various zero-shot, few-shot and task-specific baselines on the NewsClaims benchmark (Reddy et al., 2021).
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network
Authors: Rui Li, Cheng Liu, Dazhi Jiang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Domain Adaptation, Language Modeling, Language Modelling

Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
Authors: Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, Weiping Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Contrastive Learning, Few-Shot Stance Detection, Stance Detection

Stance detection aims to identify the attitude from an opinion towards a certain target. Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier. Therefore, this paper is devoted to few-shot stance detection and investigating how to achieve satisfactory results in semi-supervised settings. As a target-oriented task, the core idea of semi-supervised few-shot stance detection is to make better use of target-relevant information from labeled and unlabeled data. Therefore, we develop a novel target-aware semi-supervised framework. Specifically, we propose a target-aware contrastive learning objective to learn more distinguishable representations for different targets. Such an objective can be easily applied with or without unlabeled data. Furthermore, to thoroughly exploit the unlabeled data and facilitate the model to learn target-relevant stance features in the opinion content, we explore a simple but effective target-aware consistency regularization combined with a self-training strategy. The experimental results demonstrate that our approach can achieve state-of-the-art performance on multiple benchmark datasets in the few-shot setting.
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations
Authors: Soumitra Ghosh, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Common Sense Reasoning

Mental health is a critical component of the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 3, which aims to provide “good health and well-being”. The present mental health treatment gap is exacerbated by stigma, lack of human resources, and lack of research capability for implementation and policy reform. We present and discuss a novel task of detecting emotional reasoning (ER) and accompanying emotions in conversations. In particular, we create a first-of-its-kind multimodal mental health conversational corpus that is manually annotated at the utterance level with emotional reasoning and related emotion. We develop a multimodal multitask framework with a novel multimodal feature fusion technique and a contextuality learning module to handle the two tasks. Leveraging multimodal sources of information, commonsense reasoning, and through a multitask framework, our proposed model produces strong results. We achieve performance gains of 6% accuracy and 4.62% F1 on the emotion detection task and 3.56% accuracy and 3.31% F1 on the ER detection task, when compared to the existing state-of-the-art model.
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis
Authors: Tianhao Gao, Jun Fang, Hanyu Liu, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Yongjun Bao, Weipeng Yan
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Aspect-based sentiment analysis (ABSA) has received increasing attention recently. ABSA can be divided into multiple tasks according to the different extracted elements. Existing generative methods usually treat the output as a whole string rather than the combination of different elements and only focus on a single task at once. This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts. Further, the proposed approach can train on simple tasks and transfer to difficult tasks by assembling task prompts, like assembling Lego bricks. We conduct experiments on six ABSA tasks across multiple benchmarks. Our proposed multi-task approach achieves new state-of-the-art results in almost all tasks and competitive results in task transfer scenarios.
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations
Authors: Weixiang Zhao, Yanyan Zhao, Bing Qin
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Emotion Recognition

As an emerging research topic in natural language processing community, emotion recognition in multi-party conversations has attained increasing interest. Previous approaches that focus either on dyadic or multi-party scenarios exert much effort to cope with the challenge of emotional dynamics and achieve appealing results. However, since emotional interactions among speakers are often more complicated within the entangled multi-party conversations, these works are limited in capturing effective emotional clues in conversational context. In this work, we propose Mutual Conversational Detachment Network (MuCDN) to clearly and effectively understand the conversational context by separating conversations into detached threads. Specifically, two detachment ways are devised to perform context and speaker-specific modeling within detached threads and they are bridged through a mutual module. Experimental results on two datasets show that our model achieves better performance over the baseline models.
UECA-Prompt: Universal Prompt for Emotion Cause Analysis
Authors: Xiaopeng Zheng, Zhiyue Liu, Zizhen Zhang, Zhaoyang Wang, Jiahai Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Emotion Cause Extraction, Emotion-Cause Pair Extraction

Emotion cause analysis (ECA) aims to extract emotion clauses and find the corresponding cause of the emotion. Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. These task-specific methods have a deficiency of universality. And the relations among multiple objectives in one task are not explicitly modeled. Moreover, the relative position information introduced in most existing methods may make the model suffer from dataset bias. To address the first two problems, this paper proposes a universal prompt tuning method to solve different ECA tasks in the unified framework. As for the third problem, this paper designs a directional constraint module and a sequential learning module to ease the bias. Considering the commonalities among different tasks, this paper proposes a cross-task training method to further explore the capability of the model. The experimental results show that our method achieves competitive performance on the ECA datasets.
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification
Authors: Xiaoqin Chang, Sophia Yat Mei Lee, Suyang Zhu, Shoushan Li, Guodong Zhou
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Ensemble Learning, Knowledge Distillation, Sentiment Analysis, Sentiment Classification

Knowledge distillation is an effective method to transfer knowledge from a large pre-trained teacher model to a compacted student model. However, in previous studies, the distilled student models are still large and remain impractical in highly speed-sensitive systems (e.g., an IR system). In this study, we aim to distill a deep pre-trained model into an extremely compacted shallow model like CNN. Specifically, we propose a novel one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. Moreover, we leverage large-scale unlabeled data to improve the performance of students. Empirical studies on three sentiment classification tasks demonstrate that our approach achieves better results with much fewer parameters (0.9%-18%) and extremely high speedup ratios (100X-1000X).
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks
Authors: Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Classification, Language Modeling, Language Modelling, Sentence, Sentence Classification, text-classification, Text Classification

Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. Recent studies have explored parameter-efficient PLM tuning, which only updates a small amount of task-specific parameters while achieving both high efficiency and comparable performance against standard fine-tuning. However, all these methods ignore the inefficiency problem caused by the task-specific output layers, which is inflexible for us to re-use PLMs and introduces non-negligible parameters. In this work, we focus on the text classification task and propose plugin-tuning, a framework that further improves the efficiency of existing parameter-efficient methods with a unified classifier. Specifically, we re-formulate both token and sentence classification tasks into a unified language modeling task, and map label spaces of different tasks into the same vocabulary space. In this way, we can directly re-use the language modeling heads of PLMs, avoiding introducing extra parameters for different tasks. We conduct experiments on six classification benchmarks. The experimental results show that plugin-tuning can achieve comparable performance against fine-tuned PLMs, while further saving around 50% parameters on top of other parameter-efficient methods.
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
Authors: Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, Huijia Zhu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect Sentiment Triplet Extraction, Sentence, Triplet

Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis
Authors: Yinglong Ma, Yunhe Pang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Language Modeling, Language Modelling, Multi-Task Learning, Relation, Sentiment Analysis, Sentiment Classification

Dependency tree-based methods might be susceptible to the dependency tree due to that they inevitably introduce noisy information and neglect the rich relation information between words. In this paper, we propose a learnable dependency-based double graph (LD2G) model for aspect-based sentiment classification. We use multi-task learning for domain adaptive pretraining, which combines Biaffine Attention and Mask Language Model by incorporating features such as structure, relations and linguistic features in the sentiment text. Then we utilize the dependency enhanced double graph-based MPNN to deeply fuse structure features and relation features that are affected with each other for ASC. Experiment on four benchmark datasets shows that our model is superior to the state-of-the-art approaches.
A Structure-Aware Argument Encoder for Literature Discourse Analysis
Authors: Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Position, Representation Learning, Sentence

Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens into two groups, namely framing tokens and topic ones, to capture structural information of arguments. In addition, we consider high-level structure by incorporating paragraph-level position information. A novel structure-aware argument encoder is proposed for literature discourse analysis. Experimental results on both a self-constructed corpus and a public corpus show the effectiveness of our model. Resources are available at https://github.com/lemuria-wchen/SAE.
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
Authors: Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Contrastive Learning, Multimodal Sentiment Analysis, Sentiment Analysis

The existing research efforts in Multimodal Sentiment Analysis (MSA) have focused on developing the expressive ability of neural networks to fuse information from different modalities. However, these approaches lack a mechanism to understand the complex relations within and across different modalities, since some sentiments may be scattered in different modalities. To this end, in this paper, we propose a novel hierarchical graph contrastive learning (HGraph-CL) framework for MSA, aiming to explore the intricate relations of intra- and inter-modal representations for sentiment extraction. Specifically, regarding the intra-modal level, we build a unimodal graph for each modality representation to account for the modality-specific sentiment implications. Based on it, a graph contrastive learning strategy is adopted to explore the potential relations based on unimodal graph augmentations. Furthermore, we construct a multimodal graph of each instance based on the unimodal graphs to grasp the sentiment relations between different modalities. Then, in light of the multimodal augmentation graphs, a graph contrastive learning strategy over the inter-modal level is proposed to ulteriorly seek the possible graph structures for precisely learning sentiment relations. This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities. Experimental results on two benchmark datasets show that the proposed framework outperforms the state-of-the-art baselines in MSA.