Papers

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Viewing 21-30 of 758 papers
  • Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback

    Niket Tandon, Aman Madaan, Peter Clark, Yiming YangFindings of EMNLP 2022 Large language models (LMs), while power-ful, are not immune to mistakes, but can be difficult to retrain. Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user. Our approach pairs an LM with (i) a…
  • A Dataset for N-ary Relation Extraction of Drug Combinations

    Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…
  • Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

    Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. SmithNAACL2022 Warning : this paper discusses and contains content that is offensive or upsetting. The perceived toxicity of language can vary based on someone’s identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in…
  • Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander R. Fabbri, Yejin Choi, Noah A. SmithNAACL2022 Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments. Meanwhile, efforts to improve generation models…
  • Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer

    Yanpeng Zhao, Jack Hessel, Youngjae Yu, Ximing Lu, Rowan Zellers, Yejin ChoiNAACL2022 Machines that can represent and describe environmental soundscapes have practical poten-tial, e.g., for audio tagging and captioning. Pre-vailing learning paradigms of audio-text connections have been relying on parallel audio-text data, which is, however…
  • DEMix Layers: Disentangling Domains for Modular Language Modeling

    Suchin Gururangan, Michael Lewis, Ari Holtzman, Noah A. Smith, Luke ZettlemoyerNAACL2022 We introduce a new domain expert mixture (DEMIX) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMIX layer is a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular…
  • Efficient Hierarchical Domain Adaptation for Pretrained Language Models

    Alexandra Chronopoulou, Matthew E. Peters, Jesse DodgeNAACL2022 The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information about the source of the text is…
  • Few-Shot Self-Rationalization with Natural Language Prompts

    Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. PetersFindings of NAACL2022 Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free…
  • Literature-Augmented Clinical Outcome Prediction

    Aakanksha Naik, S. Parasa, Sergey Feldman, Lucy Lu Wang, Tom HopeFindings of NAACL 2022 We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. Based on each individual patient's clinical notes, we…
  • Long Context Question Answering via Supervised Contrastive Learning

    Avi Caciularu, Ido Dagan, Jacob Goldberger, Arman CohanNAACL2022 Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the…