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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…Twist Decoding: Diverse Generators Guide Each Other
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. SmithEMNLP • 2022 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 YuEMNLP • 2022 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 Findings • 2022 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…