"I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets

As virtual assistants continue to be taken up globally, there is an ever-greater need for these speech-based systems to communicate naturally in a variety of languages. Crowdsourcing initiatives have focused on multilingual translation of big, open data sets for use in natural language processing (NLP). Yet, language translation is often not one-to-one, and biases can trickle in. In this late-breaking work, we focus on the case of pronouns translated between English and Japanese in the crowdsourced Tatoeba database. We found that masculine pronoun biases were present overall, even though plurality in language was accounted for in other ways. Importantly, we detected biases in the translation process that reflect nuanced reactions to the presence of feminine, neutral, and/or non-binary pronouns. We raise the issue of translation bias for pronouns and offer a practical solution to embed plurality in NLP data sets.Comment: 6 page

Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent

Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most have been studied out of context using static stimuli, researcher prompts, or low fidelity prototypes. We offer a new anti-misinformation agent grounded in theories of metacognition that was evaluated within Twitter. We report on a pilot study (n=17) and multi-part experimental study (n=57, n=49) where participants experienced three versions of the agent, each deploying a different strategy. We found that no single strategy was superior over the control. We also confirmed the necessity of transparency and clarity about the agent's underlying logic, as well as concerns about repeated exposure to misinformation and lack of user engagement.Comment: Pages 209-22

"I'm" Lost in Translation: Crowdsourced Mistranslations of Pronouns in NLP Data Sets

Initiatives in natural language processing (NLP) for training natural conversation and communication between people and virtual assistants (VAs) is taking off. Many are gathering and offering large, crowdsourced data sets for this purpose. An ongoing challenge is that most are English and geared towards the US context. In recognition of this, new efforts have started to be undertaken with the goal of ensuring that a diversity of languages and cultural contexts are represented. Most are crowdsourced translation activities. However, critical analyses have raised concerns about biases in translation, notably around gender and word choice. One issue that remains under-explored is how pronouns are translated from language to language. Languages vary greatly on pronoun use. For example, in English, a small set of pronouns are commonly used, but in Japanese, pronouns are less common and arguably more varied. Translators would need to make careful choices about how to translate pronouns in a case-by-case fashion, and also be aware of the cultural context of variable pronouns such as "they/them," which can be interpreted as a neutral plural pronoun or a singular non-binary pronoun. As a first step, we evaluated pronoun translations in four open NLP data sets that were translated from English to Japanese through crowdsourcing on two respects: (i) relative number of pronouns linked to certain genders and (ii) whether or not direct translations of pronouns were faithfully preserved. We aim to raise awareness of sociocultural biases that may be embedded in translation work and offer a technical solution to account for pronoun diversity and context-sensitive variability

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T–P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T–P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required.</jats:p