译事 | 最新研究!机器翻译已对国际贸易产生重

原标题:译事 | 最新研究!机器翻译已对国际贸易产生重大影响

arXiv:1703.05260
InScript: Narrative texts annotated with script information

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This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered
around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text
is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of
the role of script knowledge in natural language processing.

如今,机器翻译发展得如火如荼,却鲜有真实的实验数据证明机器翻译到底对各行各业造成了什么样的影响。近日,来自美国国家经济研究局的研究人员率先检验了机器翻译和人工智能对贸易与工业的实质性影响,并发表了论文。他们得出了怎样的结论?欢迎关注本期译事。

InScirpt:用脚本信息注释的叙述性文本
本文介绍了一种新的语料库,InScript (Narrative Texts Instantiating Script structure)。InScript 是一个由集中在10个不同场景的1000个故事组成的语料库。动词和名词短语分别被注解为事件和参与者类型。同时,文本由易混淆的信息注解。该语料库展现了词汇多样性,同时为研究自然语言处理中脚本知识的任务提供了独特的资源。

A group of researchers at the National Bureau of Economic Research (NBER) have written a working paper entitled "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform." The NBER is a private, non-profit organization that performs and disseminates economic research and one of the most influential think tanks on economic policy in the United States.

arXiv:1703.05122
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media

美国国家经济研究局的一组研究人员写了一篇题为《机器翻译对国际贸易有影响吗?依据大型数字平台论证》的研究手稿(译者注:working paper指还未正式发表的学术论文、书刊篇章或评论)。美国国家经济研究局是一家从事并发布经济研究成果的民间非营利组织,是美国经济政策方面最有影响力的智库之一。

Code-mixing or code-switching are the effortless phenomena of natural switching between two or more languages in a single conversation. Use of a foreign word in a language; however, does not necessarily mean that the speaker is code-switching because often languages borrow lexical items from other languages. If a word is borrowed, it becomes a part of the lexicon of a language; whereas, during code-switching, the speaker is aware that the conversation involves foreign words or phrases. Identifying whether a foreign word used by a bilingual speaker is due to borrowing or code-switching is a fundamental importance to theories of multilingualism, and an essential prerequisite towards the development of language and speech technologies for multilingual communities. In this paper, we present a series of novel computational methods to identify the borrowed likeliness of a word, based on the social media signals. We first propose context based clustering method to sample a set of candidate words from the social media data.Next, we propose three novel and similar metrics based on the usage of these words by the users in different tweets; these metrics were used to score and rank the candidate words indicating their borrowed likeliness. We compare these rankings with a ground truth ranking constructed through a human judgment experiment. The Spearman’s rank correlation between the two rankings (nearly 0.62 for all the three metric variants) is more than double the value (0.26) of the most competitive existing baseline reported in the literature. Some other striking observations are, (i) the correlation is higher for the ground truth data elicited from the younger participants (age less than 30) than that from the older participants, and (ii )those participants who use mixed-language for tweeting the least, provide the best signals of borrowing.

图片 1

这个词是借用的吗?一个自动量化社交媒体上词语借来使用可能性的方法。

The paper explains that as one of the most significant technological advances today, artificial intelligence (AI) is poised to impact work, trade, and the economy. However, "empirical evidence documenting concrete economic effects of using AI is largely lacking." The study is one of the first to examine the material impact machine translation (MT), and AI in general, is having on trade and industry.

借来词已经融入的新的语言成为该语言的新的词语。而语言转化则是讲者意识到对话包括了外来词或者短语。本文中,作者提出了新的计算方法来识别一个词是借来词的可能。首先提出了基于上下文的聚类方法聚合了一些候选词,其次基于不同用户不同推特上对这些词的用法提出了三个新的相似的度量方法,这些方法可以对候选词是借来词的可能性给出评分。并且将这种评分和人工评判进行了对比试验。

这篇论文指出,人工智能作为当今一大技术进步,将对工作、贸易和经济产生影响,然而“却鲜有文献用实验证据论证人工智能对经济产生的实际影响”。该研究率先检验了机器翻译和人工智能对贸易与工业的实质性影响。

arXiv:1703.04929
SyntaxNet Models for the CoNLL 2017 Shared Task

The paper by Erik Brynjolfsson, a Professor at MIT’s Sloan School of Management, Xiang Hui, and Meng Liu was published in August 2018. It looks at how eBay’s machine translation engines have affected international trade on the platform. This analysis is used as evidence "of direct causal links between AI adoption and economic activities."

We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call “ParseySaurus,” uses the DRAGNN framework [Kong et al, 2017] to combine transition-based recurrent parsing and tagging with character-based word representations. On the v1.3 Universal Dependencies Treebanks, the new system outpeforms the publicly available, state-of-the-art “Parsey’s Cousins” models by 3.47% absolute Labeled Accuracy Score (LAS) across 52 treebanks.

该文由麻省理工学院斯隆管理学院教授Erik Brynjolfsson以及Xiang Hui和Meng Liu著成,于2018年8月发布。文章研究了eBay(译者注:美国最大的在线商品交易平台之一)的机器翻译引擎对其平台上的国际贸易有何影响。这个分析结果用于论证“人工智能应用和经济活动之间的直接因果联系”。

针对CoNll2017工作的语法网模型

Consumers Benefit

针对CoNLL2017给出了一个基线依赖解析系统。利用了DRAGNN framework [Kong et al, 2017],结合基于转换的循环分析同时利用字符的词表示来标记。新系统性能很出色。

顾客受益

arXiv:1703.04914
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples

eBay has developed its own inhouse MT engines (eMT) for multiple language pairings. The study focuses on the English to Spanish eMT, which was introduced in 2014, and looks at its effect on international trade based on US exports.

This paper describes our approach for the triple scoring task at WSDM Cup 2017. The task aims to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks. We propose an approach wherein the outputs of multiple neural network classifiers are combined using a supervised machine learning model. The experimental results show that our proposed method achieves the best performance in one out of three measures, and performs competitively in the other two measures.

eBay自主研发了多语配对机器翻译引擎。本文基于美国出口情况,主要研究了2014年引入的英语-西班牙语机器翻译引擎,及其对国际贸易的影响。

结合多个神经网络分类器在知识基准上对三个measures进行打分

According to then eBay Staff MT Language Specialist Juan Rowda in an article he wrote for Slator in November 2016, "human translation is not viable" for the company. He said as of that writing, the company had 800 million listings of around 300 words each. "It would take 1,000 translators 5 years to translate only the 60 million listings eligible for Russia," Rowda said, contextualizing the problem that eMT was meant to solve.

本论文描述了作者在2017 WSDM Cup(ACM网络搜索与数据挖掘国际会议,ACM International Conference on Web Search and Data Mining,简称WSDM)上所做的关于triple scoring工作的方法。这项工作要基于一个知识基准对每个实体对分配一个想关分数,以此来提高在实体检索工作中的排名结果。作者提出一个方法,利用一个监督的机器学习模型结合了多个神经网络分类器的输出。试验结果显示他们提出的方法在三钟测量中的一个得到了最后的结果,并且在其他两个中也具有显著的竞争性。

2016年11月,当时在eBay供职的机器翻译语言专家Juan Rowda投稿给Slator写道eBay“养不活人工翻译”。他说,eBay公司有8亿个商品,每个商品有大概300字描述。“如果雇佣1000个译员,需要5年才能仅仅翻译完给俄罗斯提供的6000万个商品。”Rowda说,机器翻译引擎正是要解决这样的问题。

arXiv:1703.04887
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

The NBER paper finds that "the introduction of a machine translation system has significantly increased international trade on this platform, increasing exports by 17.5%." Although, using a number of different control samples, the results vary from a positive change of between 11.8% and 20.9%.

This paper proposes a new route for applying the generative adversarial nets (GANs) to NLP tasks (taking the neural machine translation as an instance) and the widespread perspective that GANs can’t work well in the NLP area turns out to be unreasonable. In this work, we build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generative model (generator) which translates the source sentence into the target sentence as the traditional NMT models do and a discriminative model (discriminator) which discriminates the machine-translated target sentence from the human-translated sentence. From the perspective of Turing test, the proposed model is to generate the translation which is indistinguishable from the human-translated one. Experiments show that the proposed model achieves significant improvements than the traditional NMT model. In Chinese-English translation tasks, we obtain up to 2.0 BLEU points improvement. To the best of our knowledge, this is the first time that the quantitative results about the application of GANs in the traditional NLP task is reported. Meanwhile, we present detailed strategies for GAN training. In addition, We find that the discriminator of the proposed model shows great capability in data cleaning.

美国国家经济研究局的研究显示“机器翻译系统的引进极大促进了eBay平台的国际贸易,出口量增加了17.5%”。若使用大量不同的控制样本,该数据结果介于11.8%至20.9%之间。

利用条件序列生成对抗网来提升神经机器翻译

图片 2

本文提出一种新的方法,将生成对抗网(GAN)应用于NLP任务,也昭示所谓GANs不能再NLP领域工作的很好这一思想是没有任何道理的。在本文工作中,作者利用两个对抗支模型建立了一个条件序列生成对抗网。一个生成模型(生成器)将源语言翻译成目标语言,就像传统的神经机器翻译模型所做的那样。还有一个判别模型(判别器)将机器翻译的目标句子从人工翻译的句子中分开。按照图灵测试的思想,提出的模型应该生成与人工翻译的几乎无法区分的翻译。实验表明该模型相比于传统的神经机器翻译模型得到了很吊的提升。在汉英翻译工作中,作者获得了2 的BLEU分值的提升。据作者所知,这种定量的结果还是GANs在NLP任务应用中的首次。同时,他们还提出了一些对于GAN细节上的训练策略。并且发现判别其显示了很好的数据清洗能力。

eMT is having more of an impact on products that are differentiated, cheaper, or contain more words in listing titles, and is particularly effective for less experienced buyers, the study also found. Another interesting finding is that consumers seemed to "benefit from eMT more than sellers do […] because consumers gain both from reduced language frictions and also from lower prices."

arXiv:1703.04879
Sparse Named Entity Classification using Factorization Machines

该研究还发现,机器翻译引擎对可区分的、价格更低的或标题中包含更多词汇的产品影响更大,而且对那些缺乏购买经验的买家尤其有效。另一个有趣的发现是,买家似乎比卖家更能从机器翻译引擎中获益,因为它既能帮买家减少语言障碍,又能帮买家找到低价商品。

Named entity classification is the task of classifying text-based elements into various categories, including places, names, dates, times, and monetary values. A bottleneck in named entity classification, however, is the data problem of sparseness, because new named entities continually emerge, making it rather difficult to maintain a dictionary for named entity classification. Thus, in this paper, we address the problem of named entity classification using matrix factorization to overcome the problem of feature sparsity. Experimental results show that our proposed model, with fewer features and a smaller size, achieves competitive accuracy to state-of-the-art models.

Language Barriers Have Greatly Hindered Trade

利用分解机对稀疏命名实体进行分类

语言障碍极大阻碍了贸易

命名实体分类就是把基于文本的元素分到不同类别,包括地名人名日期时间等等。而这一任务的瓶颈是数据的稀疏性,因为新的命名实体总是在不断地出现,导致很难维护命名实体分类的词典。本文利用矩阵分解来克服特征稀疏的问题。实验表明提出的模型,只用了很少的特征和很小的模型,就获得了相对于目前state-of-the-art模型也具有竞争性的准确度。

The results in the research paper are consistent with other recent research, such as that of Johannes Lohmann in 2011 and Alejandro Molnar in 2013, who, according to the paper, "argue that language barriers may be far more trade-hindering than suggested by previous literature."

arXiv:1703.04826
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

该研究论文中的结论与最近几年其它研究得出的结论一致,比如2011年Johannes Lohmann的研究和2013年Alejandro Molnar的研究,后者在论文中写道“语言障碍对贸易的制约可能比之前文献所述更甚。”

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard natural language processing pipeline, providing information to downstream tasks such as information extraction and question answering. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of multilayer neural networks operating on graphs, suited to modeling syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence and capturing information relevant to predicting the semantic representations. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.

eMT is not used for product categories, adverts, or other fixed areas of the webpages, but is specifically applied to searches. Buyers can search in Spanish, and eMT then enables eBay to match the search query to its listing titles, which are stored in English. From here, the eMT translates and displays the listing titles in Spanish, so the Spanish users only ever need to engage with Spanish content.

利用图卷积网络编码句子来进行语义角色标记

机器翻译引擎不是用于产品目录、广告或者网页的其他固定区域,而是专用于搜索。买家可以使用西语搜索,然后机器翻译引擎能够使eBay将搜索内容与其以英语存储的商品标题列表进行匹配。接下来,机翻引擎翻译并展示西语的产品标题列表,这样西语用户就只需要浏览西语内容。

语义角色标记(SRL)就是对句子中谓词参数结构进行是被的工作。在自然语言处理流程中是很重要的一步,为下层的工作如信息抽取以及QA等提供了信息。由于语义表示和句法十分相关,作者在模型中融入了句法信息。提出了一种图卷积网络(GCNs),这是最近运行在图(graph)上的一类多层神经网络,很适合来建模句法以来的图。基于句法依赖树的GCNs可以用来对句子进行编码,生成对词潜在的特征表示,同时可以捕获跟要预测的语义表示相关的信息。作者将GCN层作为对LSTM的补充,合并GCN以及LSTM之后,相对于现在state-of-the-art的LSTM SRL模型获得了一个本质上的提升。在CoNLL-2009中标准基准测试中无论是对汉语还是英语都获得了最好的结果。

eBay’s previous translation solution for searches and item titles relied on Bing Translator, and the proprietary eMT system represents a "moderate quality upgrade", according to the paper.

arXiv:1703.04816
FastQA: A Simple and Efficient Neural Architecture for Question Answering

论文指出,eBay以前的搜索和商品标题的翻译解决方案依赖于微软必应翻译,而专有的机器翻译引擎系统意味着“质量会有一定程度的提升”。

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to a simpler neural baseline system that would justify their complexity. In this work, we propose a simple heuristic that guided the development of FastQA, an efficient end-to-end neural model for question answering that is very competitive with existing models. We further demonstrate, that an extended version (FastQAExt) achieves state-of-the-art results on recent benchmark datasets, namely SQuAD, NewsQA and MsMARCO, outperforming most existing models. However, we show that increasing the complexity of FastQA to FastQAExt does not yield any systematic improvements. We argue that the same holds true for most existing systems that are similar to FastQAExt. A manual analysis reveals that our proposed heuristic explains most predictions of our model, which indicates that modeling a simple heuristic is enough to achieve strong performance on extractive QA datasets. The overall strong performance of FastQA puts results of existing, more complex models into perspective.

The researchers also analyzed the impact of eMT in Europe, based on British and Irish exports to France, Italy and Spain, and concluded that "the rollout of eMT in Europe confirms the finding that machine translation increases exports on eBay."

快速问答:一个针对问答系统的简单有效的神经网络结构

研究人员还结合英国和爱尔兰对法国、意大利、西班牙的出口情况,分析了机器翻译引擎对欧洲的影响,从而得出结论称机器翻译引擎在欧洲上线运营,证实了机器翻译确实促进了eBay上的出口贸易这一研究结果。

最近大规模问答(QA)数据集的发展激起了很多关于QA的端到端神经网络结构的研究。问题是那些系统都特别复杂。本文中作者提出了一个简单的启发式方法,指导快速问答的研究,这是一种有效的针对QA的端到端的神经网络模型,相对于现存的模型表现出不错的竞争力。进一步表明,扩展版本(FastQAExt)在标准测试基准SQuAD,NewsQA以及MsMARCO上都获得了State-of-the-art的结果,比很多现存的模型都好。同时,作者表明从FastQA到FastQAExt增加的复杂性冰没有任何系统的提升。人工的分析揭示了他们提出的启发式的方法解释了很多他们模型的预测结果,这表明对一个简单的启发式的方法建模在已经足够在提取的QA数据集上获得很棒的表现。FastQA总体的优越表现使得现存的更复杂的模型拥有前景。

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