Maarten Sap

I am an assistant professor at CMU's LTI department. My research focuses on endowing NLP systems with social intelligence and social commonsense, and understanding social inequality and bias in language.

Before this, I was a Postdoc/Young Investigator at the Allen Institute for AI (AI2), working on project Mosaic. I received my PhD from the University of Washington where I was advised by Noah Smith and Yejin Choi, and have interned at AI2 working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.
[bio for talks]

Recent updates:

July 2022 πŸ‘¨πŸΌβ€πŸ«: I'll be attending NAACL and giving a talk about Annotators with Attitudes during session 5A: "Ethics, Bias, Fairness 1" between 14:15 – 15:45 PST Tuesday July 12

April 2022 : Giving a keynote talk at the UserNLP: User-centered Natural Language Processing Workshop collocated with the WebConf 2022 on my research! Video coming soon.

April 2022 πŸ‘¨πŸΌβ€πŸ«: I gave a talk at UPenn's Computational Linguistics Lunch (CLunch) on Detecting and Rewriting Social Biases in Language.

April 2022 πŸ“„: Excited that we have two papers accepted to NAACL 2022 in β˜” Seattle πŸ”: our preprint on annotator variation in toxicity labelling: Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection, and our new work on steering agents to do the "right thing" in text games with reinforcement learning: Aligning to Social Norms and Values in Interactive Narratives

February 2022 πŸ“„: Got two papers accepted to ACL 2022 in πŸ€ Dublin πŸ€: our paper on generating hate speech datasets with GPT-3: TOXIGEN: Controlling Language Models to Generate Implied and Adversarial Toxicity, and our paper on distilling reactions to headlines to combat misinformation: Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines

[older news]

My research group:

Ji Min Mun

LTI PhD student

Akhila Yerukola

LTI PhD student

Xuhui Zhou

LTI PhD student

Overarching Research Themes

Detecting and Mitigating Social Biases in Language

Language can perpetuate social biases and toxicity against oppressed or marginalized groups. I want to investigate new ways of representing and detecting such harmful content in text (e.g., Social Bias Frames) or in conversations (e.g., with ToxiChat). Additionally, I want to harness NLP systems to combat stereotypical or harmful statements in language, through controllable text generation (e.g., with DExperts) or controllable text debiasing (e.g., with PowerTransformer).

In the future, I want to make this technology more context-aware and human-centric, e.g., by incorporating power differentials between speaker and listener, and studying human-in-the-loop methods for toxicity detection or text debiasing.

Commonsense Reasoning for Socially Aware NLP

Through theory-of-mind, Humans are trivially able to reason about other people's intents and reactions to everyday situations. I am interested in studying how AI systems can do this type of social commonsense reasoning. For example, this requires giving models knowledge of social commensense (e.g., with Event2Mind or ATOMIC, and methods like CoMET) or social acceptibility (Social Chemistry). Additionally, this requires creating benchmarks for measuring models' social commonsense abilities (e.g., with Social IQa, or Story Commonsense).

In the future, I want to keep investigating this elusive goal of machine social commonsense. Additionally, I want to explore positive applications of this research, e.g., for therapeutic setting or for helping people with cognitive disabilities.

Analyzing the Ethics and Transparency of AI models

AI and NLP systems unfortunately encode social biases and stereotypes. I'm passionate about analyzing and diagnosing the potential negative societal impacts of these systems. For example, I've uncovered severe racial bias in hate speech detection datasets and models, and subsequently analyzed whether robustness methods for NLP can mitigate them, as well as understanding the psychological attitudes that cause over- and under-detection of content as toxic. Additionally, I've scrutinized recent pretrained language models and their training data with respect to biases, toxicity, and fake news (e.g., measuring GPT-2 and GPT-3's neural toxic degeneration, and documenting the English C4 Webtext Crawl).

In the future, I plan to keep diagnosing and mitigating the ethical, fairness, and representation issues in AI systems, especially from a human-centric perspective of end-users and other stakeholders.