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Continued Pretraining for Better Zero- and Few-Shot Promptability
Zhaofeng Wu, Robert L. Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz BeltagyEMNLP • 2022 Recently introduced language model prompting methods can achieve high accuracy in zero-and few-shot settings while requiring few to no learned task-specific parameters. Never-theless, these methods still often trail behind full model finetuning. In this work…Exploring The Landscape of Distributional Robustness for Question Answering Models
Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian H. Magnusson, Hannaneh Hajishirzi, Ludwig SchmidtFindings of EMNLP • 2022 We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a di-verse set of architectures, model sizes, and…Hyperdecoders: Instance-specific decoders for multi-task NLP
Hamish Ivison, Matthew E. PetersFindings of EMNLP • 2022 We investigate input-conditioned hypernetworks for multi-tasking in NLP, generating parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder. This approach produces a unique decoder for every input instance…GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. WeldEMNLP • 2022 While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on pro-ducing consistent evaluations that are reproducible —over time and across different…How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers
Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah Smith, Roy SchwartzEMNLP Findings • 2022 The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as…In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, Mari OstendorfEMNLP Findings • 2022 Collecting and annotating task-oriented dialogues is time-consuming and costly. Thus, zero and few shot learning for dialogue tasks presents an exciting opportunity. In this work, we propose an in-context (IC) learning framework for zero-shot and few-shot…Lexical Generalization Improves with Larger Models and Longer Training
Elron Bandel, Yoav Goldberg, Yanai ElazarFinding of EMNLP • 2022 While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challenging inputs. We analyze the use of lexical…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 EMNLP • 2022 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 KhashabiEMNLP • 2022 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…