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Mathematical reasoning is a unique aspect of human intelligence and a fundamental building block for scientific and intellectual pursuits. However, learning mathematics is often a challenging human endeavor that relies on expert instructors to create, teach and evaluate mathematical material. From an educational perspective, AI systems that aid in this process offer increased inclusion and accessibility, efficiency, and understanding of mathematics. Moreover, building systems capable of understanding, creating, and using mathematics offers a unique setting for studying reasoning in AI. This workshop will investigate the intersection of mathematics education and AI, including applications to teaching, evaluation, and assisting. Enabling these applications requires not only innovations in math AI research, but also a better understanding of the challenges in real-world education scenarios. Hence, we will bring together a group of experts from a diverse set of backgrounds, institutions, and disciplines to drive progress on these and other real-world education scenarios, and to discuss the promise and challenge of integrating mathematical AI into education.
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Tue 8:55 a.m. - 9:00 a.m.
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Introduction and Opening Remarks
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SlidesLive Video » |
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Tue 9:00 a.m. - 9:01 a.m.
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Introduction of the talk speaker
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Tue 9:01 a.m. - 9:26 a.m.
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Solving Math Problems by Joint Parsing and Cognitive Reasoning
(Invited Talk)
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SlidesLive Video » |
Song-Chun Zhu 🔗 |
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Tue 9:26 a.m. - 9:30 a.m.
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Talk Q&A
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Tue 9:30 a.m. - 9:31 a.m.
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Introduction of the talk speaker
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Tue 9:31 a.m. - 9:56 a.m.
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Natural Language Processing meets Educational Data Science
(Invited Talk)
SlidesLive Video » |
Mrinmaya Sachan 🔗 |
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Tue 9:56 a.m. - 10:00 a.m.
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Talk Q&A
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Tue 10:00 a.m. - 10:30 a.m.
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Poster Session 1
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link »
Please join us in GatherTown for our poster session. The posters are as follows: 33833 Geometric Question Answering Towards Multimodal Numerical Reasoning 33832 Towards Diagram Understanding and Cognitive Reasoning in Icon Question Answering 33830 Towards Grounded Natural Language Proof Generation 33828 Theorem-Aware Geometry Problem Solving with Symbolic Reasoning and Theorem Prediction 33827 REAL2: An end-to-end memory-augmented solver for math word problems 33826 GeoRE: A Relation Extraction Dataset for Chinese Geometry Problems 33823 MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education |
Jiaqi Chen · Tanglin Xia · Sean Welleck · Jiacheng Liu · Ran Gong · Shifeng Huang · Wei Yu · Tracy Jia Shen 🔗 |
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Tue 10:30 a.m. - 11:00 a.m.
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Coffee Break
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Tue 11:00 a.m. - 12:00 p.m.
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Interview with Stephen Wolfram
(Interview)
SlidesLive Video » |
Stephen Wolfram · Danielle R Mayer 🔗 |
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Tue 12:00 p.m. - 1:00 p.m.
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Lunch Break
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Tue 1:00 p.m. - 1:01 p.m.
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Introduction of the talk speaker
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Tue 1:01 p.m. - 1:26 p.m.
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Understanding and Knowledge Extraction from Mathematical and Scientific Text
(Invited Talk)
SlidesLive Video » |
Hanna Hajishirzi 🔗 |
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Tue 1:26 p.m. - 1:30 p.m.
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Talk Q&A
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Tue 1:30 p.m. - 1:31 p.m.
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Introduction of the talk speaker
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Tue 1:31 p.m. - 1:56 p.m.
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Free-form Grading of Math Assignments: A case study in collaboration with Art of Problem Solving
(Invited Talk)
SlidesLive Video » |
Yuri Burda 🔗 |
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Tue 1:56 p.m. - 2:00 p.m.
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Talk Q&A
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Tue 2:00 p.m. - 2:01 p.m.
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Introduction of the talk speaker
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Tue 2:01 p.m. - 2:26 p.m.
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FACT: An automated teaching assistant for middle school math classrooms
(Invited Talk)
SlidesLive Video » |
Kurt VanLehn 🔗 |
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Tue 2:26 p.m. - 2:30 p.m.
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Talk Q&A
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Tue 2:30 p.m. - 3:00 p.m.
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Poster Session 2
(Poster Session)
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Please join us in GatherTown for our poster session. The posters are as follows: 33831 Gamifying Math Education using Object Detection 33829 Who Gets the Benefit of the Doubt? Racial Bias in Machine Learning Algorithms Applied to Secondary School Math Education 33825 Phygital Math Learning with Handwriting for Kids 33824 Exploring Student Representation For Neural Cognitive Diagnosis 33822 An Empirical Study of Finding Similar Exercises 33821 Evaluation of mathematical questioning strategies using data collected through weak supervision |
Yueqiu Sun · Haewon Jeong · Nrupatunga . · Hengyao Bao · Tongwen Huang · Debajyoti Datta 🔗 |
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Tue 3:00 p.m. - 3:30 p.m.
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Coffee Break
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Tue 3:30 p.m. - 3:31 p.m.
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Introduction of the talk speaker
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Tue 3:31 p.m. - 3:56 p.m.
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Weaving AI Into Education
(Invited Talk)
SlidesLive Video » |
Sumeet Singh 🔗 |
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Tue 3:56 p.m. - 4:00 p.m.
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Talk Q&A
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Tue 4:00 p.m. - 4:01 p.m.
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Introduction of the contributed talk speaker
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Tue 4:01 p.m. - 4:16 p.m.
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MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
(Contributed Talk)
link »
SlidesLive Video » Best Paper Award for NeurIPS 2021 MathAI4Ed Workshop. Since the introduction of the original BERT (i.e., BASE BERT), researchers have developed various customized BERT models with improved performance for specific domains and tasks by exploiting the benefits of transfer learning. Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks. In this paper, we introduce our multi-institutional effort (i.e., two learning platforms and three academic institutions in the US) toward this need: MathBERT, a model created by pre-training the BASE BERT model on a large mathematical corpus ranging from pre-kindergarten (pre-k), to high-school, to college graduate level mathematical content. In addition, we select three general NLP tasks that are often used in mathematics education: prediction of knowledge component, auto-grading open-ended Q&A, and knowledge tracing, to demonstrate the superiority of m over BASE BERT. Our experiments show that MathBERT outperforms prior best methods by 1.2-22% and BASE BERT by 2-8% on these tasks. In addition, we build a mathematics specific vocabulary mathVocab to train with MathBERT. We release MathBERT for public usage at: https://github.com/tbs17/MathBERT. |
Tracy Jia Shen 🔗 |
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Tue 4:16 p.m. - 4:20 p.m.
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Contributed Talk Q&A
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Tue 4:20 p.m. - 4:21 p.m.
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Introduction of the contributed talk speaker
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Tue 4:21 p.m. - 4:36 p.m.
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Towards Grounded Natural Language Proof Generation
(Contributed Talk)
link »
SlidesLive Video » When a student is working on a mathematical proof, it is often helpful to receive suggestions about how to proceed. To this end, we provide an initial study of two generation tasks in natural mathematical language: suggesting the next step in a proof, and full-proof generation. As proofs are grounded in past results- e.g. theorems, definitions- we study knowledge-grounded generation methods, and find that conditioning on retrieved or ground-truth knowledge greatly improves generations. We characterize error types and provide directions for future research. |
Jiacheng Liu 🔗 |
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Tue 4:36 p.m. - 4:40 p.m.
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Contributed Talk Q&A
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Tue 4:40 p.m. - 5:00 p.m.
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Coffee Break
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Tue 5:00 p.m. - 6:00 p.m.
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Panel Discussion
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SlidesLive Video » |
Jo Boaler · Yuri Burda · Chris Piech · Sumeet Singh · Kurt VanLehn 🔗 |
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Tue 6:00 p.m. - 6:05 p.m.
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Closing Remarks
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Evaluation of mathematical questioning strategies using data collected through weak supervision
(Poster)
SlidesLive Video » High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, a dialogue oriented open ended conversations like teaching a student about scale factor can be difficult to model. This paper presents a high-fidelity, AI based classroom simulator to help teachers rehearse research-based mathematical questioning skills. We take a human centered approach to designing our system relying advances in deep-learning, uncertainty quantification and natural language processing while acknowledging the limitations of conversational agents for specific pedagogical needs. Using experts' input directly during the simulation, we demonstrate how conversation success rate and high user satisfaction can be achieved. |
Debajyoti Datta · Maria Phillips · James P. Bywater · Jennifer L. Chiu · Ginger S. Watson · Laura E Barnes · Donald Brown 🔗 |
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An Empirical Study of Finding Similar Exercises
(Poster)
SlidesLive Video »
Education artificial intelligence aims to profit tasks in the education domain such as intelligent test paper generation and consolidation exercises where the main technique behind is how to match the exercises, known as the finding similar exercises(FSE) problem.
Most of these approaches emphasized their model abilities to represent the exercise, unfortunately there are still many challenges such as the scarcity of data, un-sufficient understanding of exercises and high label noises. We release a Chinese education pre-trained language model BERT$_{Edu}$ for the label-scarce dataset and introduce the exercise normalization to overcome the diversity of mathematical formulas and terms in exercise. We discover new auxiliary tasks in an innovative way depends on problem-solving ideas and propose a very effective MoE enhanced multi-task model for FSE task to attain better understanding of exercises. In addition, confidence learning was utilized to prune train-set and overcome high noises in labeling data. Experiments show that these methods proposed in this paper are very effective.
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Tongwen Huang · Li Xihua · Tongwen Huang 🔗 |
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MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
(Poster)
SlidesLive Video » Since the introduction of the original BERT (i.e., BASE BERT), researchers have developed various customized BERT models with improved performance for specific domains and tasks by exploiting the benefits of transfer learning. Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks. In this paper, we introduce our multi-institutional effort (i.e., two learning platforms and three academic institutions in the US) toward this need: MathBERT, a model created by pre-training the BASE BERT model on a large mathematical corpus ranging from pre-kindergarten (pre-k), to high-school, to college graduate level mathematical content. In addition, we select three general NLP tasks that are often used in mathematics education: prediction of knowledge component, auto-grading open-ended Q&A, and knowledge tracing, to demonstrate the superiority of m over BASE BERT. Our experiments show that MathBERT outperforms prior best methods by 1.2-22% and BASE BERT by 2-8% on these tasks. In addition, we build a mathematics specific vocabulary mathVocab to train with MathBERT. We release MathBERT for public usage at: https://github.com/tbs17/MathBERT. |
Tracy Jia Shen · Michiharu Yamashita · Ethan Prihar · Neil Heffernan · Xintao Wu · Ben Graff · Dongwon Lee 🔗 |
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Exploring Student Representation For Neural Cognitive Diagnosis
(Poster)
SlidesLive Video » Cognitive diagnosis, the goal of which is to obtain the proficiency level of students on specific knowledge concepts, is an fundamental task in smart educational systems. Previous works usually represent each student as a trainable knowledge proficiency vector, which cannot capture the relations of concepts and the basic profile(e.g. memory or comprehension) of students. In this paper, we propose a method of student representation with the exploration of the hierarchical relations of knowledge concepts and student embedding. Specifically, since the proficiency on parent knowledge concepts reflects the correlation between knowledge concepts, we get the first knowledge proficiency with a parent-child concepts projection layer. In addition, a low-dimension dense vector is adopted as the embedding of each student, and obtain the second knowledge proficiency with a full connection layer. Then, we combine the two proficiency vector above to get the final representation of students. Experiments show the effectiveness of proposed representation method. |
Hengyao Bao · Li Xihua 🔗 |
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Phygital Math Learning with Handwriting for Kids
(Poster)
SlidesLive Video » To provide fun learning and concept apprehension for online education the content and experience are of prime importance. In this work, we present a Phygital (Physical + Digital) math learning through handwriting with traditional pen and paper, vital for a child's cognitive and motor skill development. Our system provides interactive educational content for 3-10 year old kids with real-time feedback and evaluation recognizing handwriting at high precision/ recall. The real-time feedback along with a virtual assisting character is developed in line with a child's thinking ability and age. Our system is used across geographies at a huge scale. |
Nrupatunga . · Aashish Kumar · Anoop Kolar Rajagopal 🔗 |
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GeoRE: A Relation Extraction Dataset for Chinese Geometry Problems
(Poster)
SlidesLive Video » Relation extraction is an important foundation for many natural language understanding applications, as well as geometry problem solving. In this paper, we present GeoRE, a relation extraction dataset for Chinese geometry problems. To the best of our knowledge, GeoRE is the first Chinese relation extraction dataset about geometry problems. It consists of 12,901 geometry problems on 43 shapes, covering 19 positional relations and 4 quantitative relations. We experiment with various state-of-the-art (SOTA) models and the best model achieves only 70.3% F1 value on GeoRE. This shows that GeoRE presents a challenge for future research. |
Wei Yu · Shuyu Miao · Xun Zhou · Jingdong Liu · Yongfu Zha · Yongjian Zhang · Mengzhu Wang · Xiaodong Wang 🔗 |
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REAL2: An end-to-end memory-augmented solver for math word problems
(Poster)
SlidesLive Video » The task of math word problems has recently shown encouraging progress, e.g. in Recall and Learn (REAL), that solving problem by retrieving most similar questions based on a pre-trained memory module. In this article, we verify the effectiveness of different neural memory modules that can be trained end-to-end. Specifically, we first propose a Top-N pre-ranking process to retrieve candidate questions based on a Word2Vec model, and then we utilize a trainable memory module to re-rank the candidates to obtain the most similar Top-K questions. With this simple modification, we establish a stronger framework REAL2 that achieves state-of-the-art results. Code will be made public and we hope it will make the research of analogical learning in MWP task more accessible. |
Shifeng Huang · Jiawei Wang · Jiao Xu · Da Cao · Ming Yang 🔗 |
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Theorem-Aware Geometry Problem Solving with Symbolic Reasoning and Theorem Prediction
(Poster)
SlidesLive Video » Geometry problem solving is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called \textit{Interpretable Geometry Problem Solver} (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project is available at https://lupantech.github.io/inter-gps. |
Pan Lu · Ran Gong · Shibiao Jiang · Liang Qiu · Siyuan Huang · Xiaodan Liang · Song-Chun Zhu · Ran Gong 🔗 |
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Who Gets the Benefit of the Doubt? Racial Bias in Machine Learning Algorithms Applied to Secondary School Math Education
(Poster)
SlidesLive Video » Machine learning algorithms are rapidly being adopted to aid pedagogical decision-making in applications ranging from grading to student placement. Are these algorithms fair? We prove that, for predicting students' math performance, the standard machine learning practice of selecting a model that maximizes predictive accuracy can result in algorithms that give significantly more benefit of the doubt to White, Asian students and are more punitive to Black, Hispanic, Native American students. This disparity is masked by comparatively high predictive accuracy across both groups. We suggest new interventions that help close this performance gap and do not require the use of a different algorithm for each student group. Together, our results suggest new best practices for applying machine learning to education-related applications. |
Haewon Jeong · Michael D. Wu · Nilanjana Dasgupta · Muriel Medard · Flavio Calmon 🔗 |
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Towards Grounded Natural Language Proof Generation
(Poster)
SlidesLive Video » When a student is working on a mathematical proof, it is often helpful to receive suggestions about how to proceed. To this end, we provide an initial study of two generation tasks in natural mathematical language: suggesting the next step in a proof, and full-proof generation. As proofs are grounded in past results- e.g. theorems, definitions- we study knowledge-grounded generation methods, and find that conditioning on retrieved or ground-truth knowledge greatly improves generations. We characterize error types and provide directions for future research. |
Sean Welleck · Jiacheng Liu · Yejin Choi 🔗 |
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Gamifying Math Education using Object Detection
(Poster)
SlidesLive Video » Manipulatives used in the right way help improve mathematical concepts leading to better learning outcomes. In this paper, we present a phygital (physical + digital) curriculum inspired teaching system for kids aged 5-8 to learn geometry using shape tile manipulatives. Combining smaller shapes to form larger ones is an important skill kids learn early on which requires shape tiles to be placed close to each other in the play area. This introduces a challenge of oriented object detection for densely packed objects with arbitrary orientations. Leveraging simulated data for neural network training and light-weight mobile architectures, we enable our system to understand user interactions and provide real-time audiovisual feedback. Experimental results show that our network runs real-time with high precision/recall on consumer devices, thereby providing a consistent and enjoyable learning experience. |
Rohitkrishna Nambiar · Yueqiu Sun · Vivek Vidyasagaran 🔗 |
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Towards Diagram Understanding and Cognitive Reasoning in Icon Question Answering
(Poster)
SlidesLive Video » Current visual question answering (VQA) tasks mainly consider answering human-annotated questions for natural images. However, aside from natural images, abstract diagrams with semantic richness are still understudied in visual understanding and reasoning research. In this work, we introduce a new challenge of Icon Question Answering (IconQA) with the goal of answering a question in an icon image context. We release IconQA, a large-scale dataset that consists of 107,439 questions, which highlights the importance of abstract diagram understanding and comprehensive cognitive reasoning. IconQA requires not only perception skills like object recognition and text understanding, but also diverse cognitive reasoning skills, such as geometric reasoning, commonsense reasoning, and arithmetic reasoning. To facilitate potential IconQA models to learn semantic representations for icon images, we further release an icon dataset Icon645 which contains 645,687 colored icons on 377 classes. We conduct extensive user studies and blind experiments and reproduce a wide range of advanced VQA methods to benchmark the IconQA task. Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset. IconQA and Icon645 are available athttps://iconqa.github.io. |
Pan Lu · Liang Qiu · Jiaqi Chen · Tanglin Xia · Yizhou Zhao · Wei Zhang · Zhou Yu · Xiaodan Liang · Song-Chun Zhu 🔗 |
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Geometric Question Answering Towards Multimodal Numerical Reasoning
(Poster)
SlidesLive Video » Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly dependent on handcraft rules and were merely evaluated on small-scale datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems. Compared with another publicly available dataset GeoS, GeoQA is 25 times larger, in which the program annotations can provide a practical testbed for future research on explicit and explainable numerical reasoning. Moreover, we introduce a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs. We further add multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic representation. Extensive experiments on GeoQA validate the effectiveness of our proposed NGS and auxiliary tasks. However, the results are still significantly lower than human performance, which leaves large room for future research. |
Jiaqi Chen · Jianheng Tang · Jinghui Qin · Xiaodan Liang · Lingbo Liu · Eric Xing · Liang Lin 🔗 |
Author Information
Pan Lu (University of California, Los Angeles)
Yuhuai Wu (Stanford University / Google)
Sean Welleck (University of Washington)
Xiaodan Liang (Sun Yat-sen University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
James McClelland (Stanford University)
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2022 Poster: NaturalProver: Grounded Mathematical Proof Generation with Language Models »
Sean Welleck · Jiacheng Liu · Ximing Lu · Hannaneh Hajishirzi · Yejin Choi -
2022 Poster: Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering »
Pan Lu · Swaroop Mishra · Tanglin Xia · Liang Qiu · Kai-Wei Chang · Song-Chun Zhu · Oyvind Tafjord · Peter Clark · Ashwin Kalyan -
2022 Poster: CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation »
Zicheng Zhang · Yi Zhu · Jianzhuang Liu · Xiaodan Liang · Wei Ke -
2021 : NaturalProofs: Mathematical Theorem Proving in Natural Language »
Sean Welleck · Jiacheng Liu · Ronan Le Bras · Hanna Hajishirzi · Yejin Choi · Kyunghyun Cho -
2021 Poster: Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals »
Lang Liu · Krishna Pillutla · Sean Welleck · Sewoong Oh · Yejin Choi · Zaid Harchaoui -
2021 Poster: Subgoal Search For Complex Reasoning Tasks »
Konrad Czechowski · Tomasz Odrzygóźdź · Marek Zbysiński · Michał Zawalski · Krzysztof Olejnik · Yuhuai Wu · Łukasz Kuciński · Piotr Miłoś -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2021 Poster: Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN »
Zhenyu Xie · Zaiyu Huang · Fuwei Zhao · Haoye Dong · Michael Kampffmeyer · Xiaodan Liang -
2021 Poster: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui -
2021 Oral: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui -
2020 : Panel Discussion & Closing »
Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing -
2020 Workshop: Self-Supervised Learning -- Theory and Practice »
Pengtao Xie · Shanghang Zhang · Pulkit Agrawal · Ishan Misra · Cynthia Rudin · Abdelrahman Mohamed · Wenzhen Yuan · Barret Zoph · Laurens van der Maaten · Xingyi Yang · Eric Xing -
2020 Poster: Regularizing Black-box Models for Improved Interpretability »
Gregory Plumb · Maruan Al-Shedivat · Ángel Alexander Cabrera · Adam Perer · Eric Xing · Ameet Talwalkar -
2020 Poster: AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning »
Hao Zhang · Yuan Li · Zhijie Deng · Xiaodan Liang · Lawrence Carin · Eric Xing -
2020 Poster: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting »
Yue Wu · Pan Zhou · Andrew Wilson · Eric Xing · Zhiting Hu -
2020 Poster: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation »
Yangxin Wu · Gengwei Zhang · Hang Xu · Xiaodan Liang · Liang Lin -
2020 Poster: Towards Interpretable Natural Language Understanding with Explanations as Latent Variables »
Wangchunshu Zhou · Jinyi Hu · Hanlin Zhang · Xiaodan Liang · Maosong Sun · Chenyan Xiong · Jian Tang -
2019 : Poster Presentations »
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor Berg-Kirkpatrick · Ruslan Salakhutdinov · Eric Xing -
2019 Poster: Learning Robust Global Representations by Penalizing Local Predictive Power »
Haohan Wang · Songwei Ge · Zachary Lipton · Eric Xing -
2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing -
2019 Poster: Learning Sample-Specific Models with Low-Rank Personalized Regression »
Ben Lengerich · Bryon Aragam · Eric Xing -
2019 Poster: Heterogeneous Graph Learning for Visual Commonsense Reasoning »
Weijiang Yu · Jingwen Zhou · Weihao Yu · Xiaodan Liang · Nong Xiao -
2019 Spotlight: Heterogeneous Graph Learning for Visual Commonsense Reasoning »
Weijiang Yu · Jingwen Zhou · Weihao Yu · Xiaodan Liang · Nong Xiao -
2018 Poster: Loss Functions for Multiset Prediction »
Sean Welleck · Zixin Yao · Yu Gai · Jialin Mao · Zheng Zhang · Kyunghyun Cho -
2018 Poster: The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models »
Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Poster: Symbolic Graph Reasoning Meets Convolutions »
Xiaodan Liang · Zhiting Hu · Hao Zhang · Liang Lin · Eric Xing -
2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Poster: Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems »
Mrinmaya Sachan · Kumar Avinava Dubey · Tom Mitchell · Dan Roth · Eric Xing -
2018 Poster: Deep Generative Models with Learnable Knowledge Constraints »
Zhiting Hu · Zichao Yang · Russ Salakhutdinov · LIANHUI Qin · Xiaodan Liang · Haoye Dong · Eric Xing -
2018 Poster: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation »
Yuan Li · Xiaodan Liang · Zhiting Hu · Eric Xing -
2018 Poster: Hybrid Knowledge Routed Modules for Large-scale Object Detection »
ChenHan Jiang · Hang Xu · Xiaodan Liang · Liang Lin -
2018 Poster: Neural Architecture Search with Bayesian Optimisation and Optimal Transport »
Kirthevasan Kandasamy · Willie Neiswanger · Jeff Schneider · Barnabas Poczos · Eric Xing -
2018 Spotlight: Neural Architecture Search with Bayesian Optimisation and Optimal Transport »
Kirthevasan Kandasamy · Willie Neiswanger · Jeff Schneider · Barnabas Poczos · Eric Xing -
2018 Poster: Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis »
Haoye Dong · Xiaodan Liang · Ke Gong · Hanjiang Lai · Jia Zhu · Jian Yin -
2018 Poster: Unsupervised Text Style Transfer using Language Models as Discriminators »
Zichao Yang · Zhiting Hu · Chris Dyer · Eric Xing · Taylor Berg-Kirkpatrick -
2017 Poster: Structured Generative Adversarial Networks »
Zhijie Deng · Hao Zhang · Xiaodan Liang · Luona Yang · Shizhen Xu · Jun Zhu · Eric Xing -
2017 Poster: Saliency-based Sequential Image Attention with Multiset Prediction »
Sean Welleck · Jialin Mao · Kyunghyun Cho · Zheng Zhang -
2016 : Eric Xing »
Eric Xing -
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices »
Kirthevasan Kandasamy · Maruan Al-Shedivat · Eric Xing -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2016 Poster: Tree-Structured Reinforcement Learning for Sequential Object Localization »
Zequn Jie · Xiaodan Liang · Jiashi Feng · Xiaojie Jin · Wen Lu · Shuicheng Yan -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Workshop: Modern Machine Learning and Natural Language Processing »
Ankur P Parikh · Avneesh Saluja · Chris Dyer · Eric Xing -
2014 Poster: On Model Parallelization and Scheduling Strategies for Distributed Machine Learning »
Seunghak Lee · Jin Kyu Kim · Xun Zheng · Qirong Ho · Garth Gibson · Eric Xing -
2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing -
2013 Poster: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Oral: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Spotlight: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Poster: A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks »
Junming Yin · Qirong Ho · Eric Xing -
2012 Workshop: Spectral Algorithms for Latent Variable Models »
Ankur P Parikh · Le Song · Eric Xing -
2012 Poster: Monte Carlo Methods for Maximum Margin Supervised Topic Models »
Qixia Jiang · Jun Zhu · Maosong Sun · Eric Xing -
2012 Poster: On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks »
Qirong Ho · Junming Yin · Eric Xing -
2012 Poster: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2012 Spotlight: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2011 Poster: Infinite Latent SVM for Classification and Multi-task Learning »
Jun Zhu · Ning Chen · Eric Xing -
2011 Poster: Kernel Embeddings of Latent Tree Graphical Models »
Le Song · Ankur P Parikh · Eric Xing -
2011 Poster: Large-Scale Category Structure Aware Image Categorization »
Bin Zhao · Li Fei-Fei · Eric Xing -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification »
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2009 Poster: Heterogeneous multitask learning with joint sparsity constraints »
Xiaolin Yang · Seyoung Kim · Eric Xing -
2009 Poster: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Spotlight: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Poster: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2009 Spotlight: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2007 Workshop: Statistical Network Models »
Kevin Murphy · Lise Getoor · Eric Xing · Raphael Gottardo -
2007 Poster: HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation »
Bing Zhao · Eric Xing -
2006 Poster: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Talk: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing