Papers

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Viewing 21-30 of 813 papers
  • Knowledge Transfer from Answer Ranking to Answer Generation

    Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro MoschittiEMNLP2022 Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This allows for synthesizing the information from multiple…
  • Lexical Generalization Improves with Larger Models and Longer Training

    Elron Bandel, Yoav Goldberg, Yanai ElazarFinding of EMNLP2022 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 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…
  • Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection

    Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro MoschittiEMNLP2022 An important task for designing QA systems is answer sentence selection (AS2): select-ing the sentence containing (or constituting) the answer to a question from a set of re-trieved relevant documents. In this paper, we propose three novel sentence-level…
  • 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…
  • Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement

    Bhavana Dalvi Mishra, Oyvind Tafjord, Peter ClarkEMNLP2022 Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our approach is to augment a QA model with a dynamic memory of…
  • 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…