Papers

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Viewing 11-20 of 219 papers
  • Modeling Context With Linear Attention for Scalable Document-Level Translation

    Zhaofeng Wu, Hao Peng, Nikolaos Pappas, Noah A. SmithFindings of EMNLP 2022 Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long documents as their attention layers have…
  • On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization

    Shruti Palaskar, Akshita Bhagia, Yonatan Bisk, Florian Metze, A. Black, Ana MarasovićFindings of EMNLP2022 Integrating vision and language has gained no-table attention following the success of pretrained language models. Despite that, a fraction of emerging multimodal models is suitable for text generation conditioned on images. This minority is typically…
  • Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

    Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, A. Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, I. Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, M. Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, S. Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hanna Hajishirzi, Daniel KhashabiEMNLP2022 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…
  • Twist Decoding: Diverse Generators Guide Each Other

    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. SmithEMNLP2022 Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models. Combining diverse models may lead to further progress, but…
  • UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

    Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao YuEMNLP2022 Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been…
  • Unsupervised Learning of Hierarchical Conversation Structure

    Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, Mari OstendorfEMNLP Findings2022 Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an…
  • WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

    Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin ChoiFindings of EMNLP2022 A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI…
  • Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection

    Suchin Gururangan, Dallas Card, Sarah K. Drier, Emily K. Gade, Leroy Z. Wang, Zeyu Wang, Luke Zettlemoyer, Noah A. SmithEMNLP2022 Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and news often serve as anchors for automatically selecting web text most…
  • Data-Efficient Finetuning Using Cross-Task Nearest Neighbors

    Hamish Ivison, Noah A. Smith, Hannaneh Hajishirzi, Pradeep DasigiarXiv2022 Language models trained on massive prompted multitask datasets like T0 (Sanh et al., 2021) or FLAN (Wei et al., 2021a) can generalize to tasks unseen during training. We show that training on a carefully chosen subset of instances can outperform training on…
  • Modeling the Machine Learning Multiverse

    Samuel J Bell, Onno P. Kampman, Jesse Dodge, Neil D. LawrenceNeurIPS2022 Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis . Our framework builds upon the multiverse analysis [1] introduced…