Zhao Meng


2022

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TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
Guirong Fu | Zhao Meng | Zhen Han | Zifeng Ding | Yunpu Ma | Matthias Schubert | Volker Tresp | Roger Wattenhofer
Proceedings of the Sixth Workshop on Structured Prediction for NLP

Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.

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Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Yu Fei | Zhao Meng | Ping Nie | Roger Wattenhofer | Mrinmaya Sachan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new situations. In this work, we show that zero-shot text classification can be improved simply by clustering texts in the embedding spaces of PLMs. Specifically, we fit the unlabeled texts with a Bayesian Gaussian Mixture Model after initializing cluster positions and shapes using class names. Despite its simplicity, this approach achieves superior or comparable performance on both topic and sentiment classification datasets and outperforms prior works significantly on unbalanced datasets. We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes. Our approach achieves an average of 20% absolute improvement over prompt-based zero-shot learning. Finally, we compare different PLM embedding spaces and find that texts are well-clustered by topics even if the PLM is not explicitly pre-trained to generate meaningful sentence embeddings. This work indicates that PLM embeddings can categorize texts without task-specific fine-tuning, thus providing a new way to analyze and utilize their knowledge and zero-shot learning ability.

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Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks
Zhao Meng | Yihan Dong | Mrinmaya Sachan | Roger Wattenhofer
Findings of the Association for Computational Linguistics: NAACL 2022

In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations for self-supervised contrastive learning. We create a word-level adversarial attack generating hard positives on-the-fly as adversarial examples during contrastive learning. In contrast to previous works, our method improves model robustness without using any labeled data. Experimental results show that our method improves robustness of BERT against four different word substitution-based adversarial attacks, and combining our method with adversarial training gives higher robustness than adversarial training alone. As our method improves the robustness of BERT purely with unlabeled data, it opens up the possibility of using large text datasets to train robust language models against word substitution-based adversarial attacks.

2021

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KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation
Yiran Xing | Zai Shi | Zhao Meng | Gerhard Lakemeyer | Yunpu Ma | Roger Wattenhofer
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)

We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task. Experimental results show that our model reaches state-of-the-art performance on the VCG task (Park et al., 2020) by applying these novel pretraining tasks.

2020

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A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples
Zhao Meng | Roger Wattenhofer
Proceedings of the 28th International Conference on Computational Linguistics

Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. In this paper, we propose a geometry-inspired attack for generating natural language adversarial examples. Our attack generates adversarial examples by iteratively approximating the decision boundary of Deep Neural Networks (DNNs). Experiments on two datasets with two different models show that our attack fools natural language models with high success rates, while only replacing a few words. Human evaluation shows that adversarial examples generated by our attack are hard for humans to recognize. Further experiments show that adversarial training can improve model robustness against our attack.

2018

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Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Zhao Meng | Lili Mou | Zhi Jin
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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How Transferable are Neural Networks in NLP Applications?
Lili Mou | Zhao Meng | Rui Yan | Ge Li | Yan Xu | Lu Zhang | Zhi Jin
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing