This paper explores the hypothesis that the diversity of human languages, right now a barrier to interoperability in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years.But this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for innovation and economic growth.
Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus
Jagiellonian University（杰洛内亚大学）；Jagiellonian Center for Quantitative Political Science（杰洛内亚量化政治科学中心）；Jagiellonian University Excellence Initiative（杰洛内亚大学卓越计划）；DigiWorld PRA；QuantPol Center（QuantPol中心）；AGH University（AGH大学）；Jagiellonian Center for Digital Humanities（杰洛内亚数字人文中心）
Carnegie Mellon University（卡内基梅隆大学）；Maulana Abul Kalam Azad University of Technology（莫拉纳·阿布·卡拉姆·阿扎德科技大学）
Polarization among US political parties, media and elites is a widely studied topic. Prominent lines of prior research across multiple disciplines have observed and analyzed growing polarization in social media. In this paper, we present a new methodology that offers a fresh perspective on interpreting polarization through the lens of machine translation. With a novel proposition that two sub-communities are speaking in two different \emph{languages}, we demonstrate that modern machine translation methods can provide a simple yet powerful and interpretable framework to understand the differenc…
Machine translation software has seen rapid progress in recent years due to the advancement of deep neural networks. People routinely use machine translation software in their daily lives, such as ordering food in a foreign restaurant, receiving medical diagnosis and treatment from foreign doctors, and reading international political news online. However, due to the complexity and intractability of the underlying neural networks, modern machine translation software is still far from robust and can produce poor or incorrect translations; this can lead to misunderstanding, financial loss, threat…
Researchers find weaknesses in current strategies for protecting privacy in large datasets. Many anonymized datasets are reidentifiable, and norms for offering data subjects notice and consent over emphasize individual responsibility. Based on fieldwork with data managers in the City of Seattle, I identify ways that these conventional approaches break down in practice. Drawing on work from theorists in sociocultural anthropology, I propose that a Human Centered Data Science move beyond concepts like dataset identifiability and sensitivity toward a broader ontology of who is implicated by a dat…
In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, the complexity and intractability of neural machine translation (NMT) models that power modern machine translation make the robustness of these systems difficult to even assess, much less guarantee. Machine translation systems can return inferior results that lead to misunderstanding, medical misdiagnoses, threats to person…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）
Machine translation between Arabic and Hebrew has so far been limited by a lack of parallel corpora, despite the political and cultural importance of this language pair. Previous work relied on manually-crafted grammars or pivoting via English, both of which are unsatisfactory for building a scalable and accurate MT system. In this work, we compare standard phrase-based and neural systems on Arabic-Hebrew translation. We experiment with tokenization by external tools and sub-word modeling by character-level neural models, and show that both methods lead to improved translation performance, wit…
Niger-Volta Language Technologies Institute（尼日尔-沃尔特语言技术研究所）
Many Nigerian languages have relinquished their previous prestige and purpose in modern society to English and Nigerian Pidgin. For the millions of L1 speakers of indigenous languages, there are inequalities that manifest themselves as unequal access to information, communications, health care, security as well as attenuated participation in political and civic life. To minimize exclusion and promote socio-linguistic and economic empowerment, this work explores the feasibility of Neural Machine Translation (NMT) for the Edoid language family of Southern Nigeria. Using the new JW300 public data…
The interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a sentence from the source Language (English) to the target language (Arabic) automatically through efficiently incorporating different statistical and Natural Language Processing (NLP) models such as language model, alignment model, phrase based model, reordering model, and translation model. These models are combined to enhance the performance of statistical machine translation (…
Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability
Sociology Department（社会学系）；Princeton University（普林斯顿大学）；Department of Communication（传播学系）；Cornell University（康奈尔大学）；Institute of Design Informatics（设计信息研究所）；University of Edinburgh（爱丁堡大学）；Department of Information Science（信息科学系）
This paper describes Lingua Custodia's submission to the WMT'19 news shared task for German-to-French on the topic of the EU elections. We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed at providing more accurate translations of specific entities like political parties and person names, given that the shared task provided no in-domain training parallel data dealing with the restricted topic. Our primary submission to the shared task uses backtranslation generated with a type of decoding allowing the insertion of constraints in…
We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19…
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；School of Informatics, Xiamen University（厦门大学信息学院）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室（厦门大学））
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
College of Application and Technology, Shenzhen University, China（应用技术学院，深圳大学，中国）；College of Big Data and Internet, Shenzhen Technology University, China（大数据与互联网学院，深圳科技大学，中国）
King’s College London（伦敦国王学院）；MIT（麻省理工学院）；Alan Turing Institute（艾伦·图灵研究所）；Queen Mary University of London（伦敦玛丽女王大学）
Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multi-cultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called…
Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a self-…
Polar: A Benchmark for Evaluating Political Bias in LLMs
Graduate School of Data Science, Seoul National University（首尔大学数据科学研究生院）；Dept. of Computer Science and Engineering, Seoul National University（首尔大学计算机科学与工程系）
Political activity on social media presents a data-rich window into political behavior, but the vast amount of data means that almost all content analyses of social media require a data labeling step. However, most automated machine classification methods ignore the multimodality of posted content, focusing either on text or images. State-of-the-art vision-and-language models are unusable for most political science research: they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework call…
