Ying Liu


Cross-modal Contrastive Attention Model for Medical Report Generation
Xiao Song | Xiaodan Zhang | Junzhong Ji | Ying Liu | Pengxu Wei
Proceedings of the 29th International Conference on Computational Linguistics

Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently. However, this image-to-text task is rather challenging due to the typical data biases: 1) Normal physiological structures dominate the images, with only tiny abnormalities; 2) Normal descriptions accordingly dominate the reports. Existing methods have attempted to solve these problems, but they neglect to exploit useful information from similar historical cases. In this paper, we propose a novel Cross-modal Contrastive Attention (CMCA) model to capture both visual and semantic information from similar cases, with mainly two modules: a Visual Contrastive Attention Module for refining the unique abnormal regions compared to the retrieved case images; a Cross-modal Attention Module for matching the positive semantic information from the case reports. Extensive experiments on two widely-used benchmarks, IU X-Ray and MIMIC-CXR, demonstrate that the proposed model outperforms the state-of-the-art methods on almost all metrics. Further analyses also validate that our proposed model is able to improve the reports with more accurate abnormal findings and richer descriptions.

Metaphor Detection via Linguistics Enhanced Siamese Network
Shenglong Zhang | Ying Liu
Proceedings of the 29th International Conference on Computational Linguistics

In this paper we present MisNet, a novel model for word level metaphor detection. MisNet converts two linguistic rules, i.e., Metaphor Identification Procedure (MIP) and Selectional Preference Violation (SPV) into semantic matching tasks. MIP module computes the similarity between the contextual meaning and the basic meaning of a target word. SPV module perceives the incongruity between target words and their contexts. To better represent basic meanings, MisNet utilizes dictionary resources. Empirical results indicate that MisNet achieves competitive performance on several datasets.

中文自然语言处理多任务中的职业性别偏见测量(Measurement of Occupational Gender Bias in Chinese Natural Language Processing Tasks)
Mengqing Guo (郭梦清) | Jiali Li (李加厉) | Jishun Zhao (赵继舜) | Shucheng Zhu (朱述承) | Ying Liu (刘颖) | Pengyuan Liu (刘鹏远)
Proceedings of the 21st Chinese National Conference on Computational Linguistics


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Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings
Jiali Li | Shucheng Zhu | Ying Liu | Pengyuan Liu
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Gender is a construction in line with social perception and judgment. An important means of this construction is through languages. When natural language processing tools, such as word embeddings, associate gender with the relevant categories of social perception and judgment, it is likely to cause bias and harm to those groups that do not conform to the mainstream social perception and judgment. Using 12,251 Chinese word embeddings as intermedium, this paper studies the relationship between social perception and judgment categories and gender. The results reveal that these grammatical gender-neutral Chinese word embeddings show a certain gender bias, which is consistent with the mainstream society’s perception and judgment of gender. Men are judged by their actions and perceived as bad, easily-disgusted, bad-tempered and rational roles while women are judged by their appearances and perceived as perfect, either happy or sad, and emotional roles.


Native Language Identification and Reconstruction of Native Language Relationship Using Japanese Learner Corpus
Mitsuhiro Nishijima | Ying Liu
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

SaGE: 基于句法感知图卷积神经网络和ELECTRA的中文隐喻识别模型(SaGE: Syntax-aware GCN with ELECTRA for Chinese Metaphor Detection)
Shenglong Zhang (张声龙) | Ying Liu (刘颖) | Yanjun Ma (马艳军)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

隐喻是人类语言中经常出现的一种特殊现象,隐喻识别对于自然语言处理各项任务来说具有十分基础和重要的意义。针对中文领域的隐喻识别任务,我们提出了一种基于句法感知图卷积神经网络和ELECTRA的隐喻识别模型(Syntax-aware GCN withELECTRA SaGE)。该模型从语言学出发,使用ELECTRA和Transformer编码器抽取句子的语义特征,将句子按照依存关系组织成一张图并使用图卷积神经网络抽取其句法特征,在此基础上对两类特征进行融合以进行隐喻识别。我们的模型在CCL2018中文隐喻识别评测数据集上以85.22%的宏平均F1分数超越了此前的最佳成绩,验证了融合语义信息和句法信息对于隐喻识别任务具有重要作用。

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K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
Song Xu | Haoran Li | Peng Yuan | Yujia Wang | Youzheng Wu | Xiaodong He | Ying Liu | Bowen Zhou
Findings of the Association for Computational Linguistics: EMNLP 2021

Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue. K-PLUG significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks. Our code is available.


Modularized Syntactic Neural Networks for Sentence Classification
Haiyan Wu | Ying Liu | Shaoyun Shi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees. In MSNN, each node of a syntax tree is modeled by a label-related syntax module. Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation. We design a tree-parallel mini-batch strategy for efficient training and predicting. Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.

用计量风格学方法考察《水浒传》的作者争议问题——以罗贯中《平妖传》为参照(Quantitive Stylistics Based Research on the Controversy of the Author of “Tales of the Marshes”: Comparing with “Pingyaozhuan” of Luo Guanzhong)
Li Song (宋丽) | Ying Liu (刘颖)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision
Jianhao Yan | Lin He | Ruqin Huang | Jian Li | Ying Liu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.


A Corpus-Based Study of zunshou and Its English Equivalents
Ying Liu
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation


A Corpus-Based Quantitative Study of Nominalizations across Chinese and British Media English
Ying Liu | Alex Chengyu Fang | Naixing Wei
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing


UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts
Bridget McInnes | Ted Pedersen | Serguei Pakhomov | Ying Liu | Genevieve Melton-Meaux
Proceedings of the 2013 NAACL HLT Demonstration Session


Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation
Bridget T. McInnes | Ted Pedersen | Ying Liu | Serguei V. Pakhomov | Genevieve B. Melton
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

The Ngram Statistics Package (Text::NSP) : A Flexible Tool for Identifying Ngrams, Collocations, and Word Associations
Ted Pedersen | Satanjeev Banerjee | Bridget McInnes | Saiyam Kohli | Mahesh Joshi | Ying Liu
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World