pwc-archive/papers-with-abstracts huggingface dataset:
An Automatic Vowel Space Generator for Language Learner Pronunciation Acquisition and Correction
Authors: Xinyuan Chao, Charbel El-Khaissi, Nicholas Kuo, Priscilla Kan John, Hanna Suominen
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12

Speech visualisations are known to help language learners to acquire correct pronunciation and promote a better study experience. We present a two-step approach based on two established techniques to display tongue tip movements of an acoustic speech signal on a vowel space plot. First we use Energy Entropy Ratio to extract vowels; and then we apply Linear Predictive Coding root method to estimate Formant 1 and Formant 2. We invited and collected acoustic data from one Modern Standard Arabic (MSA) lecture and four MSA students. Our proof of concept was able to reflect differences between the tongue tip movements in a native MSA speaker to those of a MSA language learner. This paper addresses principle methods for generating features that reflect bio-physiological features of speech and thus, facilitates an approach that can be generally adapted to languages other than MSA.
ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research
Authors: Abhishek Das, Wei Emma Zhang
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Benchmarking, Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA)has gained much attention in recent years. It is the task of identifying fine-grained opinionpolarity towards a specific aspect associated with a given target. However, there is a lack of benchmarking platform to provide a unified environment under consistent evaluation criteria for ABSA, resulting in the difficulties for fair comparisons. In this work, we address this issue and define a benchmark, ABSA-Bench, by unifying the evaluation protocols and the pre-processed publicly available datasets in a Web-based platform. ABSA-Bench provides two means of evaluations for participants to submit their predictions or models for online evaluation. Performances are ranked in the leader board and a discussion forum is supported to serve as a collaborative platform for academics and researchers to discuss queries.
A machine-learning based model to identify PhD-level skills in job ads
Authors: Li’An Chen, Inger Mewburn, Hanna Suonimen
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: BIG-bench Machine Learning

Around 60% of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to more closely align the PhD curriculum with the needs of industry, but an evidence base is lacking to inform these changes. We need to find better ways to understand what industry employers really want from doctoral graduates. One good source of data is job advertisements where employers provide a ‘wish list’ of skills and expertise. In this paper, a machine learning-natural language processing (ML-NLP) based approach was used to explore and extract skill requirements from research intensive job advertisements, suitable for PhD graduates. The model developed for detecting skill requirements in job ads was driven by SVM. The experiment results showed that ML-NLP approach had the potential to replicate manual efforts in understanding job requirements of PhD graduates. Our model offers a new perspective to look at PhD-level job skill requirements.
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets
Authors: Yuting Guo, Xiangjue Dong, Mohammed Ali Al-Garadi, Abeed Sarker, Cecile Paris, Diego Mollá Aliod
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Benchmarking, Classification, text-classification, Text Classification

Free text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. Generally speaking, performances of supervised classification algorithms on social media datasets are lower than those on texts from other sources, but recently-proposed transformer-based models have considerably improved upon legacy state-of-the-art systems. Currently, there is no study that compares the performances of different variants of transformer-based models on a wide range of social media text classification datasets. In this paper, we benchmark the performances of transformer-based pre-trained models on 25 social media text classification datasets, 6 of which are health-related. We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy. Our experiments show that RoBERTa-base and BERTweet perform comparably on most datasets, and considerably better than ClinicalBioBERT, even on health-related datasets.
Pandemic Literature Search: Finding Information on COVID-19
Authors: Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, Zhenchang Xing
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Information Retrieval, Retrieval

Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. We investigate how to better rank information for pandemic information retrieval. We experiment with different ranking algorithms and propose a novel end-to-end method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search. This work could lead to a search system that aids scientists, clinicians, policymakers and others in finding reliable answers from the scientific literature.
Information Extraction from Legal Documents: A Study in the Context of Common Law Court Judgements
Authors: Meladel Mistica, Geordie Z. Zhang, Hui Chia, Kabir Manandhar Shrestha, Rohit Kumar Gupta, Saket Khandelwal, Jeannie Paterson, Timothy Baldwin, Daniel Beck
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: text-classification, Text Classification

‘Common Law’ judicial systems follow the doctrine of precedent, which means the legal principles articulated in court judgements are binding in subsequent cases in lower courts. For this reason, lawyers must search prior judgements for the legal principles that are relevant to their case. The difficulty for those within the legal profession is that the information that they are looking for may be contained within a few paragraphs or sentences, but those few paragraphs may be buried within a hundred-page document. In this study, we create a schema based on the relevant information that legal professionals seek within judgements and perform text classification based on it, with the aim of not only assisting lawyers in researching cases, but eventually enabling large-scale analysis of legal judgements to find trends in court outcomes over time.
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition
Authors: Aaron Keesing, Ian Watson, Michael Witbrock
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Emotion Recognition, Speech Emotion Recognition

We test four models proposed in the speech emotion recognition (SER) literature on 15 public and academic licensed datasets in speaker-independent cross-validation. Results indicate differences in the performance of the models which is partly dependent on the dataset and features used. We also show that a standard utterance-level feature set still performs competitively with neural models on some datasets. This work serves as a starting point for future model comparisons, in addition to open-sourcing the testing code.
Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model
Authors: Kotaro Kitayama, Shivashankar Subramanian, Timothy Baldwin
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12

Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.
Exploring Looping Effects in RNN-based Architectures
Authors: Andrei Shcherbakov, Saliha Muradoglu, Ekaterina Vylomova
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Decoder, Language Modelling, Machine Translation, Morphological Inflection, Text Generation, Translation

The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of morphological reinflection task as a proxy to study the effects of the changes. Our results show that the probability of the occurrence of repetitive loops is significantly reduced by introduction of an extra neural decoder output. The output should be specifically trained to produce gradually increasing value upon generation of each character of a given sequence. We also explored variations of the technique and found that feeding the extra output back to the decoder amplifies the positive effects.
Transformer Semantic Parsing
Authors: Gabriela Ferraro, Hanna Suominen
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Decoder, Question Answering, Semantic Parsing

In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks. In this paper, we propose to apply the Transformer architecture, instead of recurrent neural networks, to this task. Experiments in two data sets from different domains and with different levels of difficulty show that our model achieved better results than strong baselines in certain settings and competitive results across all our experiments.
Overview of the 2020 ALTA Shared Task: Assess Human Behaviour
Authors: Diego Mollá
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12

The 2020 ALTA shared task is the 11th in stance of a series of shared tasks organised by ALTA since 2010. The task is to classify texts posted in social media according to human judgements expressed in them. The data used for this task is a subset of SemEval 2018 AIT DISC, which has been annotated by domain experts for this task. In this paper we introduce the task, describe the data and present the results of participating systems.
Automatically Predicting Judgement Dimensions of Human Behaviour
Authors: Segun Taofeek Aroyehun, Alexander Gelbukh
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: BIG-bench Machine Learning

This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models. Our submission with the Roberta-large model and prediction threshold achieved first place on the private leaderboard.
Classifying Judgements using Transfer Learning
Authors: Pradeesh Parameswaran, Andrew Trotman, Veronica Liesaputra, David Eyers
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Transfer Learning

We describe our method for classifying short texts into the APPRAISAL framework, work we conducted as part of the ALTA 2020 shared task. We tackled this problem using transfer learning. Our team, “orangutanV2” placed equal first in the shared task, with a mean F1-score of 0.1026 on the private data set.
Human Behavior Assessment using Ensemble Models
Authors: Abdullah Faiz Ur Rahman Khilji, Rituparna Khaund, Utkarsh Sinha
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: valid

Behavioral analysis is a pertinent step in today’s automated age. It is important to judge a statement on a variety of parameters before reaching a valid conclusion. In today’s world of technology and automation, Natural language processing tools have benefited from growing access to data in order to analyze the context and scenario. A better understanding of human behaviors would empower a range of automated tools to provide users a customized experience. For precise analysis, behavior understanding is important. We have experimented with various machine learning techniques, and have obtained a maximum private score of 0.1033 with a public score of 0.1733. The methods are described as part of the ALTA 2020 shared task. In this work, we have enlisted our results and the challenges faced to solve the problem of the human behavior assessment.
Eyes on the Parse: Using Gaze Features in Syntactic Parsing
Authors: Abhishek Agrawal, Rudolf Rosa
Date: 2020-12-01 00:00:00
Proceeding: LANTERN (COLING) 2020 12
Tasks: Dependency Parsing

In this paper, we explore the potential benefits of leveraging eye-tracking information for dependency parsing on the English part of the Dundee corpus. To achieve this, we cast dependency parsing as a sequence labelling task and then augment the neural model for sequence labelling with eye-tracking features. We also augment a graph-based parser with eye-tracking features and parse the Dundee Corpus to corroborate our findings from the sequence labelling parser. We then experiment with a variety of parser setups ranging from parsing with all features to a delexicalized parser. Our experiments show that for a parser with all features, although the improvements are positive for the LAS score they are not significant whereas our delexicalized parser significantly outperforms the baseline we established. We also analyze the contribution of various eye-tracking features towards the different parser setups and find that eye-tracking features contain information which is complementary in nature, thus implying that augmenting the parser with various gaze features grouped together provides better performance than any individual gaze feature.
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension
Authors: Diana Galvan-Sosa, Jun Suzuki, Kyosuke Nishida, Koji Matsuda, Kentaro Inui
Date: 2020-12-01 00:00:00
Proceeding: LANTERN (COLING) 2020 12
Tasks: Common Sense Reasoning, Machine Reading Comprehension, Natural Language Understanding, Reading Comprehension

Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.
How Do Image Description Systems Describe People? A Targeted Assessment of System Competence in the PEOPLE-domain
Authors: Emiel van Miltenburg
Date: 2020-12-01 00:00:00
Proceeding: LANTERN (COLING) 2020 12
Tasks: Image Description

Evaluations of image description systems are typically domain-general: generated descriptions for the held-out test images are either compared to a set of reference descriptions (using automated metrics), or rated by human judges on one or more Likert scales (for fluency, overall quality, and other quality criteria). While useful, these evaluations do not tell us anything about the kinds of image descriptions that systems are able to produce. Or, phrased differently, these evaluations do not tell us anything about the cognitive capabilities of image description systems. This paper proposes a different kind of assessment, that is able to quantify the extent to which these systems are able to describe humans. This assessment is based on a manual characterisation (a context-free grammar) of English entity labels in the PEOPLE domain, to determine the range of possible outputs. We examined 9 systems to see what kinds of labels they actually use. We found that these systems only use a small subset of at most 13 different kinds of modifiers (e.g. tall and short modify HEIGHT, sad and happy modify MOOD), but 27 kinds of modifiers are never used. Future research could study these semantic dimensions in more detail.
ICT’s System for AutoSimTrans 2021: Robust Char-Level Simultaneous Translation
Authors: Shaolei Zhang, Yang Feng
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Domain Adaptation, Sentence, Translation

Simultaneous translation (ST) outputs the translation simultaneously while reading the input sentence, which is an important component of simultaneous interpretation. In this paper, we describe our submitted ST system, which won the first place in the streaming transcription input track of the Chinese-English translation task of AutoSimTrans 2021. Aiming at the robustness of ST, we first propose char-level simultaneous translation and applied wait-k policy on it. Meanwhile, we apply two data processing methods and combine two training methods for domain adaptation. Our method enhance the ST model with stronger robustness and domain adaptability. Experiments on streaming transcription show that our method outperforms the baseline at all latency, especially at low latency, the proposed method improves about 6 BLEU. Besides, ablation studies we conduct verify the effectiveness of each module in the proposed method.
BIT’s system for AutoSimulTrans2021
Authors: Mengge Liu, Shuoying Chen, Minqin Li, Zhipeng Wang, Yuhang Guo
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Domain Adaptation, Sentence, Translation

In this paper we introduce our Chinese-English simultaneous translation system participating in AutoSimulTrans2021. In simultaneous translation, translation quality and delay are both important. In order to reduce the translation delay, we cut the streaming-input source sentence into segments and translate the segments before the full sentence is received. In order to obtain high-quality translations, we pre-train a translation model with adequate corpus and fine-tune the model with domain adaptation and sentence length adaptation. The experimental results on the evaluation data show that our system performs better than the baseline system.
XMU’s Simultaneous Translation System at NAACL 2021
Authors: Shuangtao Li, Jinming Hu, Boli Wang, Xiaodong Shi, Yidong Chen
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Translation

This paper describes our two systems submitted to the simultaneous translation evaluation at the 2nd automatic simultaneous translation workshop.
System Description on Automatic Simultaneous Translation Workshop
Authors: Linjie Chen, Jianzong Wang, Zhangcheng Huang, Xiongbin Ding, Jing Xiao
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Translation

This paper shows our submission on the second automatic simultaneous translation workshop at NAACL2021. We participate in all the two directions of Chinese-to-English translation, Chinese audio\rightarrowEnglish text and Chinese text\rightarrowEnglish text. We do data filtering and model training techniques to get the best BLEU score and reduce the average lagging. We propose a two-stage simultaneous translation pipeline system which is composed of Quartznet and BPE-based transformer. We propose a competitive simultaneous translation system and achieves a BLEU score of 24.39 in the audio input track.
Findings of the Second Workshop on Automatic Simultaneous Translation
Authors: Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Haifeng Wang
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Machine Translation, Speech-to-Text, Translation

This paper presents the results of the shared task of the 2nd Workshop on Automatic Simultaneous Translation (AutoSimTrans). The task includes two tracks, one for text-to-text translation and one for speech-to-text, requiring participants to build systems to translate from either the source text or speech into the target text. Different from traditional machine translation, the AutoSimTrans shared task evaluates not only translation quality but also latency. We propose a metric “Monotonic Optimal Sequence” (MOS) considering both quality and latency to rank the submissions. We also discuss some important open issues in simultaneous translation.
基于规则的双重否定识别——以“不v1不v2”为例(Double Negative Recognition Based on Rules——Taking “不v1不v2” as an Example)
Authors: Yu Wang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

“不v1不v2”是汉语中典型的双重否定结构形式之一,它包括“不+助动词+不+v2”(不得不去)、“不+是+不v2”(不是不好)、述宾结构“不v1...不v2”(不认为他不去)等多种双重否定结构,情况复杂。本文以“不v1不v2”为例,结合“元语否定”、“动词叙实性”、“否定焦点”等概念,对“不v1不v2”进行了全面的考察,制定了“不v1不v2”双重否定结构的识别策略。根据识别策略,设计了双重否定自动识别程序,并在此过程中补充了助动词表、非叙实动词表等词库。最终,对28033句语料进行了识别,识别正确率为97.87%,召回率约为93.10%。
基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia Internet Novels)
Authors: Sanle Zhang, Pengyuan Liu, Hu Zhang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

网络文学在我国发展迅猛,其数量和影响力呈现逐年上升的趋势,但目前尚无公开的较大规模网络文学作品语料库,鲜见基于语料库对网络文学具体类别作品的定量研究。本文初步建立了一个网络文学语料库,其中包括武侠和仙侠网络小说,使用文本计量、词频统计以及主题挖掘的方法对两类小说的文体风格、具体词汇使用和小说主题进行对比分析。通过比较,我们发现两类小说的文体风格大致相同,它们在词汇的使用和主题上既有共性又各具特色。从微观到宏观,从表面到内容,将定量统计和定性分析相结合,多角度、多层次的对武侠和仙侠网络小说进行比较。
基于计量的百年中国人名用字性别特征研究(A Quantified Research on Gender Characteristics of Chinese Names in A Century)
Authors: Bingjie Du, Pengyuan Liu, Yongsheng Tian
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

本文构建了一个包含11万以上条目规模的中国名人人名数据库,每条数据含有人名、性别、出生地等社会文化标签,同时含有拼音、笔画、偏旁等文字信息标签,这是目前已知最大的可用于研究的汉语真人人名数据库。基于该数据库,本文从中选择1919年至今的人名,用定性与定量结合的方法探究人名中汉字的特征和其性别差异以及历时变化。从人名长度来看,男性人名比女性人名长;从人名用字的难易度来看,女性用字比男性更复杂;从用字丰富度来看,人名用字越来越单一和集中化,男性人名的用字丰富度大于女性人名。计算人名用字的性别偏度后发现女性人名的专用自更多。两性用字意象有明显的不同,用字的意象随着时间发生改变,但改变最明显的时间节点是改革开放前后,其中女性的变化比男性显著。除此之外,我们还得出人名中的性别极性字表、各个阶段的高频字表、用字变化趋势表等。
伟大的男人和倔强的女人:基于语料库的形容词性别偏度历时研究(Great Males and Stubborn Females: A Diachronic Study of Corpus-Based Gendered Skewness in Chinese Adjectives)
Authors: Shucheng Zhu, Pengyuan Liu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

性别偏见现象是社会语言学和计算语学学者均关注的研究热点,但目前大多数研究都是基于英语的,鲜有对汉语中性别偏见现象,特别是基于形容词的研究缺乏。而形容词是衡量社会对男性和女性角色规约的有力抓手。本文首先利用调查问卷的方法,构建了一个含有466个形容词的数据集,定义性别偏度为特定形容词词义和男性或女性群体相匹配的程度,并计算了数据集中每个形容词的性别偏度。然后基于DCC语料库,研究了《人民日报》的形容词性别偏度的历时总体变化,并考察了和姓名搭配的形容词的历时变化。发现《人民日报》所使用的形容词随时间的推移整体呈现中性化趋势,但在文化大革命期间呈现非常男性化的特征,和男性姓名搭配的形容词整体呈现中性化趋势。
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)
Authors: Li Song, Ying Liu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

《水浒传》是独著还是合著,施耐庵和罗贯中是何关系一直存在争议。本文将其作者争议粗略归纳为施耐庵作、罗贯中作、施作罗续、罗作他续、施作罗改五种情况,以罗贯中的《平妖传》为参照,用假设检验、文本聚类、文本分类、波动风格计量等方法,结合对文本内容的分析,考察《水浒传》的写作风格,试图为其作者身份认定提供参考。结果显示,只有罗作他续的可能性大,即前70回为罗贯中所作,后由他人续写,其他四种情况可能性都较小。
多轮对话的篇章级抽象语义表示标注体系研究(Research on Discourse-level Abstract Meaning Representation Annotation framework in Multi-round Dialogue)
Authors: Tong Huang, Bin Li, Peiyi Yan, Tingting Ji, Weiguang Qu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Abstract Meaning Representation

对话分析是智能客服、聊天机器人等自然语言对话应用的基础课题,而对话语料与常规书面语料有较大差异,存在大量的称谓、情感短语、省略、语序颠倒、冗余等复杂现象,对句法和语义分析器的影响较大,对话自动分析的准确率相对书面语料一直不高。其主要原因在于对多轮对话缺乏严整的形式化描写方式,不利于后续的分析计算。因此,本文在梳理国内外针对对话的标注体系和语料库的基础上,提出了基于抽象语义表示的篇章级多轮对话标注体系。具体探讨了了篇章级别的语义结构标注方法,给出了词语和概念关系的对齐方案,针对称谓语和情感短语增加了相应的语义关系和概念,调整了表示主观情感词语的论元结构,并对对话中一些特殊现象进行了规定,设计了人工标注平台,为大规模的多轮对话语料库标注与计算研究奠定基础。
发音属性优化建模及其在偏误检测的应用(Speech attributes optimization modeling and application in mispronunciation detection)
Authors: Minghao Guo, Yanlu Xie
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

近年来,发音属性常常被用于计算机辅助发音训练系统(CAPT)中。本文针对使用发音属性的一些难点,提出了一种建模细颗粒度发音属性(FSA)的方法,并在跨语言属性识别、发音偏误检测中进行测试。最终,我们得到了最优平均识别准确率约为95%的属性检测器组;在两个二语测试集上的偏误检测,相比基线,基于FSA方法均获得了超过1%的性能提升。此外,我们还根据发音属性的跨语言特性设置了对照实验,并在上述任务中测试和分析。