Papers by Mark Yatskar
Gender Bias in Contextualized Word Embeddings (N19-1)
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| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
| Approach: | They conduct several intrinsic analyses to quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. |
| Outcome: | The proposed method mitigates gender bias on WinoBias probing corpus and demonstrates that it can be implemented in other systems. |
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection (2021.emnlp-tutorials)
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| Challenge: | Developing a theory of crowdsourcing for practical language problems remains an open challenge . |
| Approach: | This tutorial exposes NLP researchers to data collection crowdsourcing methods and principles through case studies. |
| Outcome: | This tutorial exposes NLP researchers to various data collection crowdsourcing methods and practices through case studies. |
Dolomites: Domain-Specific Long-Form Methodical Tasks (2025.tacl-1)
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Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, Pranesh Srinivasan, Fantine Huot, Jonathan Berant, Mark Yatskar, Dipanjan Das, Mirella Lapata, Chris Alberti
| Challenge: | Experts in various fields perform methodical writing tasks to plan, organize, and report their work. |
| Approach: | They propose a benchmark with specifications for 519 methodical writing tasks . they use expert revisions of up to 10 model-generated examples to evaluate contemporary language models. |
| Outcome: | The proposed benchmark includes specifications for 519 methodical writing tasks . it includes examples with input and output examples, and is available at https://dolomites-benchmark.github.io/ . |
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC (N19-1)
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| Challenge: | In response to this development, there have been a flurry of new datasets for question answering. |
| Approach: | They propose to use SQuAD 2.0, QuAC, and CoQA to provide question answering on textual data. |
| Outcome: | The proposed datasets provide complementary coverage of the first two aspects, but weak coverage of third. |
Visual Goal-Step Inference using wikiHow (2021.emnlp-main)
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| Challenge: | Past work in NLP examined the task of goal-step inference for textual goals . wikiHow dataset shows that goal-step inference is challenging for state-of-the-art models . |
| Approach: | They propose a task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. |
| Outcome: | The proposed task is challenging for state-of-the-art multimodal models and can be transferred to other datasets. |
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles (2020.findings-emnlp)
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| Challenge: | Recent work has shown that datasets contain incidental correlations created by idiosyncrasies in the data collection process. |
| Approach: | They propose a method that detects and ignores dataset-specific correlations by introducing a new method that makes them conditionally independent. |
| Outcome: | The proposed method detects and ignores these kinds of dataset-specific correlations, and does not require the bias to be known in advance. |
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text (2021.emnlp-main)
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Christopher Clark, Jordi Salvador, Dustin Schwenk, Derrick Bonafilia, Mark Yatskar, Eric Kolve, Alvaro Herrasti, Jonghyun Choi, Sachin Mehta, Sam Skjonsberg, Carissa Schoenick, Aaron Sarnat, Hannaneh Hajishirzi, Aniruddha Kembhavi, Oren Etzioni, Ali Farhadi
| Challenge: | Communicating with humans is challenging for AIs because of its complexity and multimodality. |
| Approach: | They propose to use a game of drawing and guessing based on Pictionary to test AIs' understanding of the world and multi-modal gestures. |
| Outcome: | The proposed game is a test for mixing language and visual/symbolic communication in AI. |
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
| Outcome: | The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback. |
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (D19-1)
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| Challenge: | Recent advances in neural models exploit dataset-specific patterns that do not generalize well to out-of-domain or adversarial settings. |
| Approach: | They propose to train a model to be more robust to domain shift if it has prior knowledge of dataset biases. |
| Outcome: | The proposed model can be more robust to domain shift if it has prior knowledge of dataset biases. |
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction (2022.findings-emnlp)
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| Challenge: | Neural language models encode rich knowledge about entities and their relationships but common properties of nouns are difficult to extract because they are rarely explicitly stated in texts. |
| Approach: | They propose to extract perceptual properties from images and use them in an ensemble model to complement the information extracted from language models. |
| Outcome: | The proposed model improves noun property prediction compared to powerful text-based language models. |
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (N18-2)
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| Challenge: | Existing methods for co-reference resolution focus on gender bias. |
| Approach: | They propose a new benchmark for co-reference resolution focused on gender bias, WinoBias. |
| Outcome: | The proposed system removes the bias without significantly affecting performance on existing datasets. |
QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
| Outcome: | The proposed dataset underperforms humans in a number of reference models . the dataset contains 14K information-seeking dialogs over sections from Wikipedia . |
Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D (2025.emnlp-main)
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Artemis Panagopoulou, Le Xue, Honglu Zhou, Silvio Savarese, Ran Xu, Caiming Xiong, Chris Callison-Burch, Mark Yatskar, Juan Carlos Niebles
| Challenge: | a recent study shows that multimodal models can reason across multiple modalities . a limited number of models are able to reason across a variety of inputs . |
| Approach: | They propose a dataset for contrastive cross-modal reasoning across four modalities . they use human annotations and a mixture-of-models round-trip-consistency filter . |
| Outcome: | a new model evaluates models on multiple modalities to determine which one best answers a natural language prompt . the model must select the one that best satisfies the query and then fine-tune it . state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modal settings . |
AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference (2023.findings-eacl)
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| Challenge: | Existing models do not have welldefined target behavior for coreferential ambiguity. |
| Approach: | They propose to use AmbiCoref to test whether coreference resolution models are sensitive to ambiguity. |
| Outcome: | The proposed model is more sensitive to ambiguity than existing models. |
Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation (2025.acl-long)
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Yue Yang, Ajay Patel, Matt Deitke, Tanmay Gupta, Luca Weihs, Andrew Head, Mark Yatskar, Chris Callison-Burch, Ranjay Krishna, Aniruddha Kembhavi, Christopher Clark
| Challenge: | Vision-language models struggle to understand text-rich images due to the scarcity of diverse text-only large language data. |
| Approach: | They propose a framework that leverages the coding capabilities of text-only large language models to create synthetic text-rich multimodal data. |
| Outcome: | The proposed framework can generate high-quality instruction-tuning data using Python, HTML, LaTeX and other languages. |
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models (2022.emnlp-main)
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| Challenge: | Cognitive psychologists have documented that humans use cognitive heuristics to make quick decisions while expending less effort. |
| Approach: | They propose tracking annotator heuristic traces where they measure low-effort annotation strategies that could indicate usage of various cognitive heurs. |
| Outcome: | The proposed tracking annotator heuristic traces shows that annotators are using multiple cognitive heurs based on psychological tests. |
What Does BERT with Vision Look At? (2020.acl-main)
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| Challenge: | Pre-trained visual grounded language models have improved performance on vision-and-language tasks but what they learn during pre-training remains unclear. |
| Approach: | They show that attention heads of visual grounded language models actively ground elements of language to image regions. |
| Outcome: | The attention heads of a visual grounded language model can ground elements to image regions, demonstrating their ability to detect syntactic relations between non-entity words and image regions. |
ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)
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| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |