Sexism affects both women and men, yet research often overlooks misandry and suffers from overly broad annotations that limit AI applications. To address this, we introduce BeyondGender, a dataset meticulously annotated according to the latest definitions of misogyny and misandry. It features innovative multifaceted labels encompassing aspects of sexism, gender, phrasing, misogyny, and misandry. The dataset includes 6K English and 1.7K Chinese sexism instances, alongside 13K non-sexism examples. Our evaluations of masked language models and large language models reveal that they detect misogyny in English and misandry in Chinese more effectively, with F1-scores of 0.87 and 0.62, respectively. However, they frequently misclassify hostile and mild comments, underscoring the complexity of sexism detection. Parallel corpus experiments suggest promising data augmentation strategies to enhance AI systems for nuanced sexism detection, and our dataset can be leveraged to improve value alignment in large language models.

In this article, we present our work in social emotion prediction to online trending topics. While most prior works focus on emotion from writers or the readers’ emotions evoked by news articles, we investigate discussions from massive social media users and explore the public feelings to the online trending topic. We employ user-generated “#hashtags” to indicate online trending topics and construct a large-scale Chinese dataset for social emotion prediction (LCSEP) to trending topics collected from the Chinese microblog Sina Weibo. It contains more than 20 000 trending topics, each with social emotions voted in 24 fine-grained types, and gathers hashtags, posts, comments, and related metadata to give each trending topic a thorough context. We also propose aHashtag- and Topic-Enhanced Attention Model(HTEAM) that combines a pretrained BERT model, a neural topic model, and an attention mechanism via joint training to understand social emotion. Experiments show that HTEAM outperforms baselines and achieves the state-of-the-art result.

Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.

Coreference resolution is a challenging yet practical problem. Most previous methods are designed to better utilize sequential features of language but can hardly capture the structural associations between mentions. In addition, it is often observed that during long-term training, the embeddings projected from unrelated mentions tend to move closer or even mix together, which increases the difficulty of learning decision boundaries. To tackle these issues: i) We propose a general graph schema derived from diverse knowledge sources (e.g., lemma, type, and semantic roles) to directly link mentions, so that rich information can be exchanged via the relevant connections; ii) We impose two adaptive constraints during graph encoding to regularize the embedding space. One is used to force different sub-modules to generate consistent predictions for the same mention pairs, and the other aims to make the learned embeddings corresponding to unrelated mentions more distinguishable while those of coreferential mentions more similar. Results on two public datasets (ECB+ and ACE05) show that our model consistently outperforms state-of-the-art baselines under different settings with$p$-value less than 0.01 in$t$-test, especially learning effectively from the limited labeled data.

Jianzhu Bao, Chuang Fan, Jipeng Wu, Yixue Dang, Jiachen Du, Ruifeng Xu. 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.

Auxiliary information from multiple sources has been demonstrated to be effective in zeroshot fine-grained entity typing (ZFET).However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET.In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources.The performance obtains up to 11.42% and 22.84% absolute gains over stateof-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.

The task emotion-cause pair extraction deals with finding all emotions and the corresponding causes from emotion texts. Existing joint methods solve it as multi-task learning, which introduces two auxiliary tasks (i.e., emotion extraction and cause extraction) to make use of task correlations for their mutual benefits. However, these methods focus on capturing such correlations by sharing parameters in an implicit way, not only have a limitation of cannot explicitly model their information interaction, but also suffer from low interpretability. Towards these issues, we propose a multi-task sequence tagging framework, which can extract emotions with the associated causes simultaneously by encoding their distances into a novel tagging scheme. In addition, the output of both auxiliary tasks can be directly used as inductive bias, to refine the tag distribution for benefiting emotion-cause pair extraction, so that the information exchange between them can be more explicit and interpretable. Results show that our model achieves the best performance, outperforming a number of competitive baselines by at least 1.03% ($p< 0.01$) in$F_1$score. The comprehensive analysis further confirms the superiority and robustness of our model.

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation.Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction.The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently.Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p < 0.01) in F 1 measure.

The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts.Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost.Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously.Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up.Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p < 0.001) in F 1 measure.

Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, Ruibin Mao. 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.

Memory networks have shown expressive performance on aspect based sentiment analysis. However, ordinary memory networks only capture word-level information and lack the capacity for modeling complicated expressions which consist of multiple words. Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism. This model sequentially computes the weights of multiple memory units corresponding to multi-words. This model may capture both words and multi-words expressions in sentences for aspect-based sentiment analysis. Experimental results show that the proposed model outperforms the state-of-the-art baselines.