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DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models
Gregor Betz, Kyle RichardsonSEM • 2022 In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst – a T5 model (Raffel et al. 2020) set up and trained within DeepA2…Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations
Spencer K. Clark, Noah D. Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Oliver Watt-Meyer, Christopher S. Bretherton, Lucas M. Harris Earth and Space Science Open Archive • 2022 Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ~200 km version of NOAA’s FV3GFS) evolve more like a…Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
Sheshera Mysore, Arman Cohan, Tom HopeNAACL • 2022 We present a new scientific document similarity model based on matching fine-grained aspects of texts. To train our model, we exploit a naturally-occurring source of supervision: sentences in the full-text of papers that cite multiple papers together (co…A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, Roozbeh MottaghiarXiv • 2022 The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. Despite a proliferation of VQA datasets, this goal is hindered by a set of…VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug DowneyTACL • 2022 Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into…What Language Model to Train if You Have One Million GPU Hours?
Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, Saiful Bari, Stella Rose Biderman, Hady ElSahar, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang A. Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz BeltagyEMNLP • 2022 The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale, increasing the impact of modeling research. However, with the…Quark: Controllable Text Generation with Reinforced Unlearning
Ximing Lu, S. Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin ChoiNeurIPS • 2022 Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition, or be of a different sentiment than desired by the user. We consider the task…Retrieval Data Augmentation Informed by Downstream Question Answering Performance
James Ferguson, Pradeep Dasigi, Tushar Khot, Hannaneh HajishirziACL • FEVER • 2022 Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling relevant passages in those corpora. Since obtaining exhaustive manual annotations of all relevant passages is not feasible, prior work uses text…Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, Arman CohanACL • 2022 Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges…Investigating the Benefits of Free-Form Rationales
Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe MaarXiv • 2022 Free-form rationales aim to aid model interpretability by supplying the background knowledge that can help understand model decisions. Crowdsourced rationales are provided for commonsense QA instances in popular datasets such as CoS-E and ECQA, but their…