Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.5
Nikoletta Ventoura, Kosmas Palios, Yannis Vasilakis, G. Paraskevopoulos, Nassos Katsamanis, V. Katsouros
Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.
{"title":"Theano: A Greek-speaking conversational agent for COVID-19","authors":"Nikoletta Ventoura, Kosmas Palios, Yannis Vasilakis, G. Paraskevopoulos, Nassos Katsamanis, V. Katsouros","doi":"10.18653/v1/2021.nlp4posimpact-1.5","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.5","url":null,"abstract":"Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128034122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.7
Yusra Anees, Sadaf Abdul Rauf
To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. We present a lexical and syntactically simplified Urdu simplification corpus with a detailed analysis of the various simplification operations and human evaluation of corpus quality. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified and syntactically simplified corpora. In addition, we compare our corpus with other existing simplification corpora by building simplification systems and evaluating these systems using BLEU and SARI scores. Our system achieves the highest BLEU score and comparable SARI score in comparison to other systems. We release our simplification corpora for the benefit of the research community.
{"title":"Automatic Sentence Simplification in Low Resource Settings for Urdu","authors":"Yusra Anees, Sadaf Abdul Rauf","doi":"10.18653/v1/2021.nlp4posimpact-1.7","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.7","url":null,"abstract":"To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. We present a lexical and syntactically simplified Urdu simplification corpus with a detailed analysis of the various simplification operations and human evaluation of corpus quality. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified and syntactically simplified corpora. In addition, we compare our corpus with other existing simplification corpora by building simplification systems and evaluating these systems using BLEU and SARI scores. Our system achieves the highest BLEU score and comparable SARI score in comparison to other systems. We release our simplification corpora for the benefit of the research community.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128549237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.11
Renzhe Yu, Suprotik Das, Sairam Gurajada, Kush R. Varshney, Hari Raghavan, Carlos X. Lastra‐Anadón
Understanding the gaps between job requirements and university curricula is crucial for improving student success and institutional effectiveness in higher education. In this context, natural language processing (NLP) can be leveraged to generate granular insights into where the gaps are and how they change. This paper proposes a three-dimensional research framework that combines NLP techniques with economic and educational research to quantify the alignment between course syllabi and job postings. We elaborate on key technical details of the framework and further discuss its potential positive impacts on practice, including unveiling the inequalities in and long-term consequences of education-occupation alignment to inform policymakers, and fostering information systems to support students, institutions and employers in the school-to-work pipeline.
{"title":"A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques","authors":"Renzhe Yu, Suprotik Das, Sairam Gurajada, Kush R. Varshney, Hari Raghavan, Carlos X. Lastra‐Anadón","doi":"10.18653/v1/2021.nlp4posimpact-1.11","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.11","url":null,"abstract":"Understanding the gaps between job requirements and university curricula is crucial for improving student success and institutional effectiveness in higher education. In this context, natural language processing (NLP) can be leveraged to generate granular insights into where the gaps are and how they change. This paper proposes a three-dimensional research framework that combines NLP techniques with economic and educational research to quantify the alignment between course syllabi and job postings. We elaborate on key technical details of the framework and further discuss its potential positive impacts on practice, including unveiling the inequalities in and long-term consequences of education-occupation alignment to inform policymakers, and fostering information systems to support students, institutions and employers in the school-to-work pipeline.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130193497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.6
Myrthe Reuver, Nicolas Mattis, M. Sax, S. Verberne, N. Tintarev, N. Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, Wouter van Atteveldt
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate.To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.
{"title":"Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content","authors":"Myrthe Reuver, Nicolas Mattis, M. Sax, S. Verberne, N. Tintarev, N. Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, Wouter van Atteveldt","doi":"10.18653/v1/2021.nlp4posimpact-1.6","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.6","url":null,"abstract":"In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate.To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134070255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.4
Tommaso Caselli, R. Cibin, Costanza Conforti, Enrique Encinas, Maurizio Teli
We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems. The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle. While principles 1–3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6. Principles 4–6 describes the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annotation, and the deployment & evaluation. Finally, principles 7–9 guide a new reflexivity of the NLP research with respect to its context, actors and participants, and aims. We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.
我们介绍了将参与式设计(PD)方法整合到自然语言处理(NLP)系统开发中的9条指导原则。NLP采用PD方法将有助于缓解有关开发更民主、更公平、更少偏见的技术来处理自然语言数据的问题。这篇短文是设计师和NLP专家之间持续对话的结果，并采用了Traum(2000)之前工作的非标准格式;本德(2013);Abzianidze and Bos(2019)。每一节都是一个指导原则。虽然原则1-3说明了为社区PD实践提供信息的假设和方法，但我们使用了两个虚构的设计场景(Encinas和Blythe, 2018)，它们建立在作者熟悉的情况之上，以引出对其他6个的识别。原则4-6描述了PD方法对NLP系统设计的影响，针对两个关键方面:数据收集和注释，以及部署和评估。最后，原则7-9指导了关于其背景、行动者和参与者以及目标的NLP研究的新反思。我们希望本指南能够为开发新一代PD-inspired NLP提供灵感和路线图。
{"title":"Guiding Principles for Participatory Design-inspired Natural Language Processing","authors":"Tommaso Caselli, R. Cibin, Costanza Conforti, Enrique Encinas, Maurizio Teli","doi":"10.18653/v1/2021.nlp4posimpact-1.4","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.4","url":null,"abstract":"We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems. The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle. While principles 1–3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6. Principles 4–6 describes the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annotation, and the deployment & evaluation. Finally, principles 7–9 guide a new reflexivity of the NLP research with respect to its context, actors and participants, and aims. We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121962066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.2
Manfred Stede, R. Patz
The debate around climate change (CC)—its extent, its causes, and the necessary responses—is intense and of global importance. Yet, in the natural language processing (NLP) community, this domain has so far received little attention. In contrast, it is of enormous prominence in various social science disciplines, and some of that work follows the ”text-as-data” paradigm, seeking to employ quantitative methods for analyzing large amounts of CC-related text. Other research is qualitative in nature and studies details, nuances, actors, and motivations within CC discourses. Coming from both NLP and Political Science, and reviewing key works in both disciplines, we discuss how social science approaches to CC debates can inform advances in text-mining/NLP, and how, in return, NLP can support policy-makers and activists in making sense of large-scale and complex CC discourses across multiple genres, channels, topics, and communities. This is paramount for their ability to make rapid and meaningful impact on the discourse, and for shaping the necessary policy change.
{"title":"The Climate Change Debate and Natural Language Processing","authors":"Manfred Stede, R. Patz","doi":"10.18653/v1/2021.nlp4posimpact-1.2","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.2","url":null,"abstract":"The debate around climate change (CC)—its extent, its causes, and the necessary responses—is intense and of global importance. Yet, in the natural language processing (NLP) community, this domain has so far received little attention. In contrast, it is of enormous prominence in various social science disciplines, and some of that work follows the ”text-as-data” paradigm, seeking to employ quantitative methods for analyzing large amounts of CC-related text. Other research is qualitative in nature and studies details, nuances, actors, and motivations within CC discourses. Coming from both NLP and Political Science, and reviewing key works in both disciplines, we discuss how social science approaches to CC debates can inform advances in text-mining/NLP, and how, in return, NLP can support policy-makers and activists in making sense of large-scale and complex CC discourses across multiple genres, channels, topics, and communities. This is paramount for their ability to make rapid and meaningful impact on the discourse, and for shaping the necessary policy change.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122337275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.3
Paula Fortuna, Laura Pérez-Mayos, Ahmed Ghassan Tawfiq AbuRa'ed, Juan Soler-Company, L. Wanner
The range of works that can be considered as developing NLP for social good (NLP4SG) is enormous. While many of them target the identification of hate speech or fake news, there are others that address, e.g., text simplification to alleviate consequences of dyslexia, or coaching strategies to fight depression. However, so far, there is no clear picture of what areas are targeted by NLP4SG, who are the actors, which are the main scenarios and what are the topics that have been left aside. In order to obtain a clearer view in this respect, we first propose a working definition of NLP4SG and identify some primary aspects that are crucial for NLP4SG, including, e.g., areas, ethics, privacy and bias. Then, we draw upon a corpus of around 50,000 articles downloaded from the ACL Anthology. Based on a list of keywords retrieved from the literature and revised in view of the task, we select from this corpus articles that can be considered to be on NLP4SG according to our definition and analyze them in terms of trends along the time line, etc. The result is a map of the current NLP4SG research and insights concerning the white spots on this map.
{"title":"Cartography of Natural Language Processing for Social Good (NLP4SG): Searching for Definitions, Statistics and White Spots","authors":"Paula Fortuna, Laura Pérez-Mayos, Ahmed Ghassan Tawfiq AbuRa'ed, Juan Soler-Company, L. Wanner","doi":"10.18653/v1/2021.nlp4posimpact-1.3","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.3","url":null,"abstract":"The range of works that can be considered as developing NLP for social good (NLP4SG) is enormous. While many of them target the identification of hate speech or fake news, there are others that address, e.g., text simplification to alleviate consequences of dyslexia, or coaching strategies to fight depression. However, so far, there is no clear picture of what areas are targeted by NLP4SG, who are the actors, which are the main scenarios and what are the topics that have been left aside. In order to obtain a clearer view in this respect, we first propose a working definition of NLP4SG and identify some primary aspects that are crucial for NLP4SG, including, e.g., areas, ethics, privacy and bias. Then, we draw upon a corpus of around 50,000 articles downloaded from the ACL Anthology. Based on a list of keywords retrieved from the literature and revised in view of the task, we select from this corpus articles that can be considered to be on NLP4SG according to our definition and analyze them in terms of trends along the time line, etc. The result is a map of the current NLP4SG research and insights concerning the white spots on this map.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122707601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18653/v1/2021.nlp4posimpact-1.15
P. Bouillon, Johanna Gerlach, Jonathan Mutal, Nikos Tsourakis, H. Spechbach
In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.
{"title":"A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility","authors":"P. Bouillon, Johanna Gerlach, Jonathan Mutal, Nikos Tsourakis, H. Spechbach","doi":"10.18653/v1/2021.nlp4posimpact-1.15","DOIUrl":"https://doi.org/10.18653/v1/2021.nlp4posimpact-1.15","url":null,"abstract":"In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.","PeriodicalId":133017,"journal":{"name":"Proceedings of the 1st Workshop on NLP for Positive Impact","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121262377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：