Liang Yang


2021

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Locality Preserving Sentence Encoding
Changrong Min | Yonghe Chu | Liang Yang | Bo Xu | Hongfei Lin
Findings of the Association for Computational Linguistics: EMNLP 2021

Although researches on word embeddings have made great progress in recent years, many tasks in natural language processing are on the sentence level. Thus, it is essential to learn sentence embeddings. Recently, Sentence BERT (SBERT) is proposed to learn embeddings on the sentence level, and it uses the inner product (or, cosine similarity) to compute semantic similarity between sentences. However, this measurement cannot well describe the semantic structures among sentences. The reason is that sentences may lie on a manifold in the ambient space rather than distribute in an Euclidean space. Thus, cosine similarity cannot approximate distances on the manifold. To tackle the severe problem, we propose a novel sentence embedding method called Sentence BERT with Locality Preserving (SBERT-LP), which discovers the sentence submanifold from a high-dimensional space and yields a compact sentence representation subspace by locally preserving geometric structures of sentences. We compare the SBERT-LP with several existing sentence embedding approaches from three perspectives: sentence similarity, sentence classification and sentence clustering. Experimental results and case studies demonstrate that our method encodes sentences better in the sense of semantic structures.

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结合标签转移关系的多任务笑点识别方法(Multi-task punchlines recognition method combined with label transfer relationship)
Tongyue Zhang (张童越) | Shaowu Zhang (张绍武) | Bo Xu (徐博) | Liang Yang (杨亮) | Hongfei Lin (林鸿飞)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

幽默在人类交流中扮演着重要角色,并大量存在于情景喜剧中。笑点(punchline)是情景喜剧实现幽默效果的形式之一,在情景喜剧笑点识别任务中,每条句子的标签代表该句是否为笑点,但是以往的笑点识别工作通常只通过建模上下文语义关系识别笑点,对标签的利用并不充分。为了充分利用标签序列中的信息,本文提出了一种新的识别方法,即结合条件随机场的单词级-句子级多任务学习模型,该模型在两方面进行了改进,首先将标签序列中相邻两个标签之间的转移关系看作幽默理论中不一致性的一种体现,并使用条件随机场学习这种转移关系,其次由于学习相邻标签之间的转移关系以及上下文语义关系均能够学习到铺垫和笑点之间的不一致性,两者之间存在相关性,为了使模型通过利用这种相关性提高笑点识别的效果,该模型引入了多任务学习方法,使用多任务学习方法同时学习每条句子的句义、组成每条句子的所有字符的词义,单词级别的标签转移关系以及句子级别的标签转移关系。本文在CCL2020“小牛杯”幽默计算—情景喜剧笑点识别评测任务的英文数据集上进行实验,结果表明,本文提出的方法比目前最好的方法提高了3.2%,在情景喜剧幽默笑点识别任务上取得了最好的效果,并通过消融实验证明了上述两方面改进的有效性。

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基于风格化嵌入的中文文本风格迁移(Chinese text style transfer based on stylized embedding)
Chenguang Wang (王晨光) | Hongfei Lin (林鸿飞) | Liang Yang (杨亮)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

对话风格能够反映对话者的属性,例如情感、性别和教育背景等。在对话系统中,通过理解用户的对话风格,能够更好地对用户进行建模。同样的,面对不同背景的用户,对话机器人也应该使用不同的语言风格与之交流。语言表达风格是文本的内在属性,然而现有的大多数文本风格迁移研究,集中在英文领域,在中文领域则研究较少。本文构建了三个可用于中文文本风格迁移研究的数据集,并将多种已有的文本风格迁移方法应用于该数据集。同时,本文提出了基于DeepStyle算法与Transformer的风格迁移模型,通过预训练可以获得不同风格的隐层向量表示。并基于Transformer构建生成端模型,在解码阶段,通过重建源文本的方式,保留生成文本的内容信息,并且引入对立风格的嵌入表示,使得模型能够生成不同风格的文本。实验结果表明,本文提出的模型在构建的中文数据集上均优于现有模型。

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面向法律文本的实体关系联合抽取算法(Joint Entity and Relation Extraction for Legal Texts)
Wenhui Song (宋文辉) | Xiang Zhou (周翔) | Ping Yang (杨萍) | Yuanyuan Sun (孙媛媛) | Liang Yang (杨亮) | Hongfei Lin (林鸿飞)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

法律文本中包含的丰富信息可以通过结构化的实体关系三元组进行表示,便于法律知识的存储和查询。传统的流水线方法在自动抽取三元组时执行了大量冗余计算,造成了误差传播。而现有的联合学习方法无法适用于有大量重叠关系的法律文本,也并未关注语法结构信息对文本表示的增强,因此本文提出一种面向法律文本的实体关系联合抽取模型。该模型首先通过ON-LSTM注入语法信息,然后引入多头注意力机制分解重叠关系。相较于流水线和其他联合学习方法本文模型抽取效果最佳,在涉毒类法律文本数据集上抽取结果的F1值达到78.7%。

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软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
Dongzhen Wen (汶东震) | Fan Zhang (张帆) | Xiao Zhang (张晓) | Liang Yang (杨亮) | Yuan Lin (林原) | Bo Xu (徐博) | Hongfei Lin (林鸿飞)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

软件源代码的理解则是软件协同开发与维护的核心,而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用,传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上,提出一种全新的标识符可理解性评价标准。具体而言,本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程,即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上,本文提出一种结合自然语料库的软件标识符规范性评价方法,用来衡量软件标识符是否易于理解。最后,本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验,结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。

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MultiMET: A Multimodal Dataset for Metaphor Understanding
Dongyu Zhang | Minghao Zhang | Heting Zhang | Liang Yang | Hongfei Lin
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)

Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging. As a means of cognition, metaphor is rendered by more than texts alone, and multimodal information in which vision/audio content is integrated with the text can play an important role in expressing and understanding metaphor. However, previous metaphor processing and understanding has focused on texts, partly due to the unavailability of large-scale datasets with ground truth labels of multimodal metaphor. In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. It contains 10,437 text-image pairs from a range of sources with multimodal annotations of the occurrence of metaphors, domain relations, sentiments metaphors convey, and author intents. MultiMET opens the door to automatic metaphor understanding by investigating multimodal cues and their interplay. Moreover, we propose a range of strong baselines and show the importance of combining multimodal cues for metaphor understanding. MultiMET will be released publicly for research.

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Hate Speech Detection Based on Sentiment Knowledge Sharing
Xianbing Zhou | Yang Yong | Xiaochao Fan | Ge Ren | Yunfeng Song | Yufeng Diao | Liang Yang | Hongfei Lin
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)

The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.

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Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification
Shuqun Li | Liang Yang | Weidong He | Shiqi Zhang | Jingjie Zeng | Hongfei Lin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent metaphor identification approaches mainly consider the contextual text features within a sentence or introduce external linguistic features to the model. But they usually ignore the extra information that the data can provide, such as the contextual metaphor information and broader discourse information. In this paper, we propose a model augmented with hierarchical contextualized representation to extract more information from both sentence-level and discourse-level. At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation. At the discourse level, the position-aware global memory network is adopted to learn long-range dependency among the same words within a discourse. Finally, our model combines the representations obtained from these two parts. The experiment results on two tasks of the VUA dataset show that our model outperforms every other state-of-the-art method that also does not use any external knowledge except what the pre-trained language model contains.

2020

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ALBERT-BiLSTM for Sequential Metaphor Detection
Shuqun Li | Jingjie Zeng | Jinhui Zhang | Tao Peng | Liang Yang | Hongfei Lin
Proceedings of the Second Workshop on Figurative Language Processing

In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.

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基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)
Jinhui Zhang (张瑾晖) | Shaowu Zhang (张绍武) | Xiaochao Fan (樊小超) | Liang Yang (杨亮) | Hongfei Lin (林鸿飞)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展,幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体,忽视了幽默文本内部的语义关系。本文将幽默等级识别视为自然语言推理任务,将幽默文本划分为“铺垫”和“笑点”两个部分,分别对其语义和语义关系进行建模,提出了一种多粒度语义交互理解网络,从单词和子句两个粒度捕获幽默文本中语义的关联和交互。本文在Reddit公开幽默数据集上进行了实验,相比之前最优结果,模型在语料上的准确率提升了1.3%。实验表明,引入幽默内部的语义关系信息可以提高模型幽默识别的性能,而本文提出的模型也可以很好地建模这种语义关系。

2019

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Transformer-Based Capsule Network For Stock Movement Prediction
Jintao Liu | Hongfei Lin | Xikai Liu | Bo Xu | Yuqi Ren | Yufeng Diao | Liang Yang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

2018

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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
Yufeng Diao | Hongfei Lin | Di Wu | Liang Yang | Kan Xu | Zhihao Yang | Jian Wang | Shaowu Zhang | Bo Xu | Dongyu Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.

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Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
Dongyu Zhang | Hongfei Lin | Liang Yang | Shaowu Zhang | Bo Xu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Metaphors are frequently used to convey emotions. However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions. Furthermore, most studies focus on English, and few in other languages, particularly Sino-Tibetan languages such as Chinese, for emotion analysis from metaphorical texts, although there are likely to be many differences in emotional expressions of metaphorical usages across different languages. We therefore construct a significant new corpus on metaphor, with 5,605 manually annotated sentences in Chinese. We present an annotation scheme that contains annotations of linguistic metaphors, emotional categories (joy, anger, sadness, fear, love, disgust and surprise), and intensity. The annotation agreement analyses for multiple annotators are described. We also use the corpus to explore and analyze the emotionality of metaphors. To the best of our knowledge, this is the first relatively large metaphor corpus with an annotation of emotions in Chinese.