Yan Zhang


2021

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利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)
Gechao Wang (王屹超) | Muhua Zhu (朱慕华) | Chen Xu (许晨) | Yan Zhang (张琰) | Huizhen Wang (王会珍) | Jingbo Zhu (朱靖波)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“视觉问答作为多模态任务,需要深度理解图像和文本问题从而推理出答案。然而在许多情况下,仅在图像和问题上进行简单推理难以得到正确的答案,事实上还有其它有效的信息可以被利用,例如图像描述、外部知识等。针对以上问题,本文提出了利用图像描述和外部知识增强表示的视觉问答模型。该模型以问题为导向,基于协同注意力机制分别在图像和其描述上进行编码,并且利用知识图谱嵌入,将外部知识编码到模型当中,丰富了模型的特征表示,增强模型的推理能力。在OKVQA数据集上的实验结果表明本文方法相比基线系统有1.71%的准确率提升,与先前工作中的主流模型相比也有1.88%的准确率提升,证明了本文方法的有效性。”

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Bootstrapped Unsupervised Sentence Representation Learning
Yan Zhang | Ruidan He | Zuozhu Liu | Lidong Bing | Haizhou Li
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)

As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. Specifically, given one augmented view of the input sentence, the online network branch is trained by predicting the representation yielded by the target network of the same sentence under another augmented view. Meanwhile, the target network branch is bootstrapped with a moving average of the online network. The proposed method significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks. It can be adopted as a post-training procedure to boost the performance of the supervised methods. We further extend our method for learning multilingual sentence representations and demonstrate its effectiveness on cross-lingual STS tasks. Our code is available at https://github.com/yanzhangnlp/BSL.

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DynaEval: Unifying Turn and Dialogue Level Evaluation
Chen Zhang | Yiming Chen | Luis Fernando D’Haro | Yan Zhang | Thomas Friedrichs | Grandee Lee | Haizhou Li
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)

A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.

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ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization
Liwen Xu | Yan Zhang | Lei Hong | Yi Cai | Szui Sung
Proceedings of the 20th Workshop on Biomedical Language Processing

In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION). First, the candidate sentences are roughly estimated by using the Roberta model. Then the Markov chain model is used to evaluate the sentences in a fine-grained manner. Our team won the first place in overall performance, with the fourth place in MAS task, the seventh place in RRS task and the eleventh place in QS task. For the QS and RRS tasks, we investigate the performanceS of the end-to-end pre-trained seq2seq model. Experiments show that the methods of adversarial training and reverse translation are beneficial to improve the fine tuning performance.

2020

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ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Liying Cheng | Dekun Wu | Lidong Bing | Yan Zhang | Zhanming Jie | Wei Lu | Luo Si
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.

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An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang | Ruidan He | Zuozhu Liu | Kwan Hui Lim | Lidong Bing
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence BERT (SBERT) attempted to solve this challenge by learning semantically meaningful representations of single sentences, such that similarity comparison can be easily accessed. However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce. In this paper, we propose a lightweight extension on top of BERT and a novel self-supervised learning objective based on mutual information maximization strategies to derive meaningful sentence embeddings in an unsupervised manner. Unlike SBERT, our method is not restricted by the availability of labeled data, such that it can be applied on different domain-specific corpus. Experimental results show that the proposed method significantly outperforms other unsupervised sentence embedding baselines on common semantic textual similarity (STS) tasks and downstream supervised tasks. It also outperforms SBERT in a setting where in-domain labeled data is not available, and achieves performance competitive with supervised methods on various tasks.

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Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang | Zhijiang Guo | Zhiyang Teng | Wei Lu | Shay B. Cohen | Zuozhu Liu | Lidong Bing
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution Networks (GCNs) were used to encode input AMRs, however, vanilla GCNs are not able to capture non-local information and additionally, they follow a local (first-order) information aggregation scheme. To account for these issues, larger and deeper GCN models are required to capture more complex interactions. In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local interactions by synthesizing higher order information from the input graphs. We further develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity. With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity. Experiments demonstrate that LDGCNs outperform state-of-the-art models on two benchmark datasets for AMR-to-text generation with significantly fewer parameters.

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Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou | Yankai Lin | Shaobo Liu | Peng Li | Jie Zhou | Yan Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-based continual graph representation learning (DiCGRL) framework inspired by the human’s ability to learn procedural knowledge. The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models. The code and datasets are released on https://github.com/KXY-PUBLIC/DiCGRL.

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“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
Yuntao Li | Bei Chen | Qian Liu | Yan Gao | Jian-Guang Lou | Yan Zhang | Dongmei Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users’ natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.

2019

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Attention Guided Graph Convolutional Networks for Relation Extraction
Zhijiang Guo | Yan Zhang | Wei Lu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.

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Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
Zhijiang Guo | Yan Zhang | Zhiyang Teng | Wei Lu
Transactions of the Association for Computational Linguistics, Volume 7

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.

2018

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Discriminating between Similar Languages on Imbalanced Conversational Texts
Junqing He | Xian Huang | Xuemin Zhao | Yan Zhang | Yonghong Yan
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2015

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User Based Aggregation for Biterm Topic Model
Weizheng Chen | Jinpeng Wang | Yan Zhang | Hongfei Yan | Xiaoming Li
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Tailor knowledge graph for query understanding: linking intent topics by propagation
Shi Zhao | Yan Zhang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Summarizing Complex Events: a Cross-Modal Solution of Storylines Extraction and Reconstruction
Shize Xu | Shanshan Wang | Yan Zhang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Dialog State Tracking using Conditional Random Fields
Hang Ren | Weiqun Xu | Yan Zhang | Yonghong Yan
Proceedings of the SIGDIAL 2013 Conference

2011

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Combining Syntactic and Semantic Features by SVM for Unrestricted Coreference Resolution
Huiwei Zhou | Yao Li | Degen Huang | Yan Zhang | Chunlong Wu | Yuansheng Yang
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Timeline Generation through Evolutionary Trans-Temporal Summarization
Rui Yan | Liang Kong | Congrui Huang | Xiaojun Wan | Xiaoming Li | Yan Zhang
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2005

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Corpus-oriented Acquisition of Chinese Grammar
Yan Zhang | Hideki Kashioka
Proceedings of the Fifth Workshop on Asian Language Resources (ALR-05) and First Symposium on Asian Language Resources Network (ALRN)

2002

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Chinese Syntactic Parsing Based on Extended GLR Parsing Algorithm with PCFG*
Yan Zhang | Bo Xu | Chengqing Zong
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes