Pegah Alipoormolabashi


2022

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Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals
Te-Lin Wu | Alex Spangher | Pegah Alipoormolabashi | Marjorie Freedman | Ralph Weischedel | Nanyun Peng
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures. It is essential for applications such as task planning and multi-source instruction summarization. It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images. While humans are capable of reasoning about and sequencing unordered procedural instructions, the extent to which the current machine learning methods possess such capability is still an open question. In this work, we benchmark models’ capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations. We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing multimodal information. To improve machines’ performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in > 5% improvements on perfect match ratio.

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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang | Swaroop Mishra | Pegah Alipoormolabashi | Yeganeh Kordi | Amirreza Mirzaei | Atharva Naik | Arjun Ashok | Arut Selvan Dhanasekaran | Anjana Arunkumar | David Stap | Eshaan Pathak | Giannis Karamanolakis | Haizhi Lai | Ishan Purohit | Ishani Mondal | Jacob Anderson | Kirby Kuznia | Krima Doshi | Kuntal Kumar Pal | Maitreya Patel | Mehrad Moradshahi | Mihir Parmar | Mirali Purohit | Neeraj Varshney | Phani Rohitha Kaza | Pulkit Verma | Ravsehaj Singh Puri | Rushang Karia | Savan Doshi | Shailaja Keyur Sampat | Siddhartha Mishra | Sujan Reddy A | Sumanta Patro | Tanay Dixit | Xudong Shen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.

2021

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COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Shikhar Singh | Nuan Wen | Yu Hou | Pegah Alipoormolabashi | Te-lin Wu | Xuezhe Ma | Nanyun Peng
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds
Pegah Alipoormolabashi | Sabine Schulte im Walde
Proceedings of the Fourth Widening Natural Language Processing Workshop

Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.