Neural networks might be impressive linguistic mimics, but they clearly lacked basic common sense. language. Social media is increasingly used for large-scale population predictions... Social norms—the unspoken commonsense rules about acceptable social We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. (2018), who use a bootstrapping approach to sample potentially offensive tweets. Deep learning has brought a wealth of state-of-the-art results and new capabilities. Thread Reader Discover and read the best of Twitter Threads about #nlproc. Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi • ACL • 2019 We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et … our standards to hire more women," most listeners will infer the implicature Large scale crowdsourcing and characterization of twitter abusive behavior. Journal of medical Internet research 17 (2), e51, 2015. Controlling other people. In Safiya Umoja Noble and Brendesha M Tynes, editors. COMET: Commonsense Transformers for Automatic Knowledge Graph Construction. Although methods have achieved near human-level performance on many benchmarks, numerous recent studies imply that these benchmarks only weakly test their intended purpose, and that simple examples produced either by human or machine, cause systems to fail spectacularly. interaction. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Discover and read the best of Twitter Threads about #nlproc. ∙ explicitly, but all the implied meanings that frame people's judgements about 0 ∙ During training, we minimize the negative log-likelihood of the predictions: During inference, we simply predict the classes which have highest probability. support systems. projects unwanted social bias (86 ∙ Representing general relational knowledge in conceptnet 5. This long-overdue blog post is based on the Commonsense Tutorial taught by Maarten Sap, Antoine Bosselut, Yejin Choi, Dan Roth, and myself at ACL 2020.Credit for much of the content goes to the co-instructors, but any errors are mine. ∙ stereotypes on others. To mitigate the challenge of scarcity of online toxicity Founta et al. or sexual references are a key subcategory of what constitutes potentially offensive material in many cultures, especially in the United States Strub (2008). Large scale crowdsourcing and characterization of twitter abusive 2011. SBIC supports large scale learning and evaluation with over 100k structured annotations of social media posts, spanning over 26k implications about a thousand demographic groups. Tell me more? Presenting our new V+L pretraining work: “Unifying Vision-and-Language Tasks via Text Generation”, ... Antoine Bosselut … An T Nguyen, Aditya Kharosekar, Saumyaa Krishnan, Siddhesh Krishnan, Elizabeth We show example inference tuples in Table 1. This is motivated by previous work on how speaker identity influences how a statement is received Greengross and Miller (2008); Sap et al. Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. In addition, we introduce the Social Bias Inference and Joelle Pineau. share. We introduce Social Bias Frames, a new conceptual formalism that aims to With the appointment of Antoine Bosselut, EPFL is enhancing its expertise in this field of research. from a single thread. Antoine J Allen, age 32, Minneapolis, MN 55432 View Full Report Known Locations: Minneapolis MN, 55432, Spring Lake Park MN 55432, Saint Paul MN 55125 we design a new pragmatic formalism that distinguishes several related but distinct inferences, shown in Figure 1. He has initiated an unsupervised machine learning paradigm that combines accurate simulation of electronic structures with long time and large scale models, allowing the description of disordered or out-of-equilibrium systems His work thus opens new avenues for the synthesis of molecules and alloys, finding applications in industry. ∙ Determining offensiveness and reasoning about harmful implications of language should be done with care. ∙ Anjalie Field, Gayatri Bhat, and Yulia Tsvetkov. Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, jewish folks have large noses; jewish folks have the same features; jews are fun to joke about; makes fun of there looks; stereotype about there nose length; they have big noses, are not pleasant people; fat folks are all the same; fat folks are less than others; not likable. Integrating social power into the decision-making of cognitive Neural networks might be impressive linguistic mimics, but they clearly lacked basic common sense. 2008. Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi. This is a categorical variable with two possible answers. We train or finetune our models using a batch size of 4, a learning rate of 5e-5 (with linear warm up), and consider training for. Follow us on Instagram. Understanding these biases with accurate underlying explanations is necessary for AI systems to adequately interact in the social world Pereira et al. 2019. (2019a); Davidson et al. obscenity in cold war los angeles. We additionally compute word mover’s distance (WMD; Kusner et al., 2015), which uses distributed word representations to measure similarity between the generated and target text. ∙ When deploying such algorithms, several ethical aspects should be considered including the fairness of the model on speech by different demographic groups or in different varieties of English Mitchell et al. How NOT to evaluate your dialogue system: An empirical study of (2017); Founta et al. ... Twitter sentiment predicts Affordable Care Act marketplace enrollment. thousand demographic groups. Choi. Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, and Yejin Choi. Psychological Language on Twitter Predicts County-level Heart Disease Mortality. Additionally, we include posts from three existing English datasets annotated for toxic or abusive language, filtering out @-replies, retweets, and links. In contrast to reasoning about particular individuals, our work focuses on biased implications of social and demographic groups as a whole. describes the social or demographic group that is referenced or targeted by the post. 03/06/2020 ∙ by Julia Mendelsohn, et al. Vered Shwartz. In addition, we introduce SBIC,111Social Bias Inference Corpus, available at http://tinyurl.com/social-bias-frames. Antoine Bosselut Computer Science Dallas Card Computer Science Chris Donahue Computer Science Vivek ... Twitter Victor Zhong, Computer Science. ∙ We collect free-text answers in the form of simple Hearst-like patterns (e.g., “women are ADJ”, “gay men VBP”; Hearst, 1992). Narrative paths and negotiation of power in birth stories. are distinguished from individual-only attacks or insults that do not invoke power dynamics between groups TalkDown: A corpus for condescension detection in context. Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias We draw from two sources of online content, namely Reddit and Twitter, to select posts to annotate. The full task is shown in the supplementary (Figure 5). Journal of Medical Internet Research, 17(2), JMIR Publications Inc.. @article{wong2015twitter, We use BLEU-2 and RougeL (F1) scores to capture word overlap between the generated inference and the references, which captures quality of generation Galley et al. The way such biases are projected is rarely in what is stated explicitly, but in all the implied layers of meanings that frame and influence people’s judgements about others. His research focuses on building systems for commonsense knowledge representation and reasoning that combine the strengths of modern neural and traditional symbolic methods. 2018. 07/23/2019 ∙ by Chenwei Zhang, et al. Mrs. Mundy: ”You’re a disgrace to your race, Marcus”, are good at basketball; black men are defined by athletic skill. (2019b), we use automated metrics to evaluate model generations. Winogrande: An adversarial winograd schema challenge at scale. Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. Workers marked posts as lewd with substantial agreement (94%, κ=0.66), but agreed less when marking the speaker a minority (94%, κ=0.18).555Low κ values are expected for highly skewed categories such as minority speaker (only 4% “yes”). Language has the power to reinforce stereotypes and project social biases We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Psychological Science Charlene A Wong, Maarten Sap, Hansen Andrew Schwartz, Robert Town, Tom Baker, Lyle Ungar & Raina M #Islam, [”bad people.”, ”islam promotes controlling governments”, ”muslims are authoritarians”, ”not fair.”], ”Black guy in class: *attempts to throw a paper ball into the trash* 2015 IEEE International Conference on Computer Vision (ICCV), Join one of the world's largest A.I. personality, and sex on the Short-Term and Long-Term attractiveness of Twitter Holds An Inauguration Celebration For Michelle Obama’s Silk Press. beha... Dehumanization is a pernicious psychological process that often leads to... Gang-involved youth in cities such as Chicago sometimes post on social m... Learning commonsense knowledge from natural language text is nontrivial ... Nowadays, with the booming development of the Internet, people benefit f... With an outreach in more than 90 countries, a market share of 2.1 billio... Kendrick just dropped the coldest video of all fucking time, What a fucking stupid bitch shut the fuck up already, need to fuck sum bad , I got some pressure built up :P, You annoyed me every day for months you’re a fucking moron, My problem with Korean artists: I dont know how to pronounce your name I can’t hanggul to save my life. and Ritesh Kumar. Discover and read the best of Twitter Threads about #nlproc. Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. The clearly obscene and the queerly obscene: Heteronormativity and Thou shalt not discriminate: How emphasizing moral ideals rather than His numerous articles in well-regarded publications have become standard works of references and his research has been awarded an ERC Starting Grant and the EPFL’s Latsis Prize. CONAN - COunter NArratives through nichesourcing: a Yi-Ling Chung, Elizaveta Kuzmenko, Serra Sinem Tekiroglu, and Marco Guerini. aims to flag posts for which the speaker may be part of the same social group referenced. Antoine Bosselut Postdoctoral Young Investigator. We establish baseline performance of models built on top of large pretrained language model. These encoders are connected to a single […] We design a hierarchical Amazon Mechanical Turk (MTurk) framework to collect biased implications of a given post (snippet shown in Figure 2. intended by the speaker - that "women (candidates) are less qualified." Cooperative generator-discriminator networks for abstractive phenomena in social media posts. Connotation frames of power and agency in modern films. In addition, it is the detailed explanations that are much more informative for people to understand and reason about why a statement is potentially harmful against other people Ross et al. Simulating Action Dynamics with Neural Process Networks. (2019) and condescension Wang and Potts (2019). Sort. Self-Deprecating and Other-Deprecating humor. ∙ 25 ∙ share . (2019). Stefanie Ullmann and Marcus Tomalin. @jmin__cho. (2019). model the pragmatic frames in which people project social biases and 2019b. We also compare the full multitask model to a baseline generative inference model trained only on the language modelling loss (L2). 2016. 2019. Racial bias in hate speech and abusive language detection datasets. Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. Most notably for classification, the multitask model outperforms other variants substantially when predicting a post’s offensiveness and intent to offend (+8% F1 on both). others. This indicates that more sophisticated models are required for Social Bias Frames inferences. We find that while state-of-the-art neural models are effective at making high-level categorization of whether a given statement projects unwanted social bias (86% F1), they are not effective at spelling out more detailed explanations by accurately decoding out Social Bias Frames. Articles Cited by Co-authors. 2019a. Specifically, we frame the inference as a conditional language modelling task, by appending the linearized targeted group (g) and implied statement (s) to the post (using the SEP delimiter token; see Figure 4). Oleg Yazyev is a highly creative researcher and a dedicated teacher and mentor. Most dataset creation work has cast this detection problem as binary classification Waseem and Hovy (2016); Wulczyn et al. As expected, using the randomly initialized model performs significantly worse than the pretrained version. Archie Ruiz, Mylène Sebagh, Raphaël Saffroy, Marc-Antoine Allard, Philippe Bouvet De La Maisonneuve, Nelly Bosselut, René Adam, Antoinette Lemoine and Jean-François Morere Cancer Res May 1 2015 (75) (9 Supplement) P4-05-14; DOI: 10.1158/1538-7445.SABCS14-P4-05-14 Professor Oleg Yazyev was named as Associate Professor of Theoretical Physics in the School of Basic Sciences (SB) Oleg Yazyev is one of the world’s leading researchers in condensed matter physics. His work is highly innovative and has made a significant contribution to the present level of knowledge in this relatively new area of research, including our understanding of the role played by epigenetic changes in the development of Alzheimer’s disease. speech detection on twitter. For classification, we report precision, recall, and F1 scores of the positive class.Following previous generative inference work Sap et al. We compute how well annotators agreed on categorical questions, showing moderate agreement on average. Dissing oneself versus dissing rivals: Effects of status, unsupervised evaluation metrics for dialogue response generation. For the free-text variables, we take inspiration from recent generative commonsense modelling Bosselut et al. We hypothesize that correctly predicting those might require more lexical matching (e.g., detecting sexual words for the lewd category). 2017. This is a categorical variable with four possible answers (yes, probably, probably not, no). Gabriel et al. What breaks first? 2019. Thus, we propose Social Bias Frames, a novel conceptual formalism that aims to model pragmatic frames in which people project social biases and stereotypes on others. While SBIC covers a variety of types of biases, gender-based, race-based, and culture-based biases are the most represented, which parallels the types of discrimination happening in the real world RWJF (2017). 0 ... Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment. Most previous approaches to understanding the implied harm in statements have cast this task as a simple toxicity classification set, and report performance for the best performing setting (according to average F1). Ex machina: Personal attacks seen at scale. The first Safety for Conversational AI Workshop will be held virtually on October 15th, 2020, 10:00am-3:00pm EDT. Professor Johannes Gräff was named as Associate Professor of Life Sciences in the School of Life Sciences (SV) Johannes Gräff has gained international recognition as a neuroscientist who has done pioneering work on the epigenetic bases of memory. 2019. ∙ I completed my PhD at the University of … (2019). I’ll continue this thread till non-lifting fags stop lifting like idiots at the gym… Which is never. Ellery Wulczyn, Nithum Thain, and Lucas Dixon. The classification-only model slightly outperforms the full multitask model on other categories. Sequence generation is important because we need it to solve real life problems such as machine translation, document summarization , question generation , sentence generation, as well as many image and video captioning tasks that are developed at MSR in the last years. We collect a new dataset of 100k annotations on social media posts using a novel crowdsourcing framework. Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, Our encoder model then yields a contextualized representation of each token hi=fe(wi∣p)∈RH, where H is the hidden size of the encoder. online# MeToo stories. represents the power dynamic or stereotype that is referenced in the post. (2019). PhD student, University of Washington Postdocs. His laboratory is an important and conspicuous component of the study of molecular neuroscience at EPFL. ∙ Large scale crowdsourcing and characterization of twitter abusive behavior. Therefore, the important premise we take in this study is that assessing social media content through the lens of Social Bias Frames is important for automatic flagging or AI-augmented writing interfaces, where potentially harmful online contents can be analyzed with detailed explanations for users to consider and verify. (2019). which people express social biases and power differentials in language. ∙ I thought drugs were the only things black people could shoot up Boy was I wrong, All-female casts bother me but I have literally never noticed the hundreds of movies with 0 female characters I have already watched, women aren’t good actors; women can’t be in leading roles, As expected, when the terrorist group Hamas won the election in Gaza it was the usual one man, one vote, one time, Islamist election. share, Gang-involved youth in cities such as Chicago sometimes post on social m... SACs: David Bamman, Chenhao Tan ACs: Dallas Card, Paramveer Dhillon, Lucie Flek, Kenny Joseph, David Mimno, Dong Nguyen, Brendan O’Connor, Daniel Preotiuc-Pietro, Sara Tonelli, Svetlana Volkova, Justine Zhang Dialogue and Interactive Systems. Event2mind: Commonsense inference on events, intents, and reactions. Marcus posted the exchanges to his Twitter account with his own added commentary: “LMAO,” internet slang for a derisive chortle. 0 Maria Antoniak, David Mimno, and Karen Levy. Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. Among these applications, he has collaborated with CERN by developing control algorithms for particle guidance based on techniques developed by his team. 11/01/2020 ∙ by Maxwell Forbes, et al. agents. (2019a). ; do not take things seriously. Particularly, previous work has analyzed power dynamics about specific entities, either in conversation settings (Prabhakaran et al., 2014; Danescu-Niculescu-Mizil et al., 2012) or in narrative text Sap et al. Predicting the type and target of offensive posts in social media. (2018) as our encoder fe, which has yielded impressive classification and generation results Radford et al. Cooperative generator-discriminator networks for abstractive summarization with … Comet: Commonsense transformers for automatic knowledge graph 2017. ∙ Optionally, we ask workers for coarse-grained demographic information.444This study was approved by the University of Washington IRB. Here we collect free-text answers, but provide a seed list of demographic or social groups to encourage consistency. Find out more about EMNLP 2020 and discover the best artificial intelligence and machine learning event alternatives. A survey on hate speech detection using natural language processing. Finding microaggressions in the wild: A case for locating elusive study motivates future research that combines structured pragmatic inference We show a general overview of the full model in Figure. share, With an outreach in more than 90 countries, a market share of 2.1 billio... Antoine Bosselut, Stanford University, USA Christophe Gravier, Universite de Saint-Etienne/Lyon, France. From word embeddings to document distances. Antoine Bosselut, Jianfu Chen, David Warren, Hannaneh Hajishirzi and Yejin Choi: 15:50-16:10: Cross-Lingual Image Caption Generation Takashi Miyazaki and Nobuyuki Shimizu: 16:10-16:30: Learning Concept Taxonomies from Multi-modal Data Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan and Eric Xing: 16:30-16:50