Papers

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Viewing 21-30 of 173 papers
  • PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts

    Daniel Khashabi, Shan Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, Yejin ChoiNAACL2022 Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous…
  • UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training

    Daniel Khashabi, Yeganeh Kordi, Hannaneh HajishirziarXiv2022 We present UNIFIEDQA-v2, a QA model built with the same process as UNIFIEDQA, except that it utilizes more supervision – roughly 3× the number of datasets used for UNIFIEDQA. This generally leads to better in-domain and cross-domain results.1
  • FLEX: Unifying Evaluation for Few-Shot NLP

    Jonathan Bragg, Arman Cohan, Kyle Lo, Iz BeltagyNeurIPS2021 Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental design. Consequently, the community does not know which…
  • Natural Adversarial Objects

    Felix Lau, Nishant Subramani, Sasha Harrison, Aerin Kim, E. Branson, Rosanne LiuNeurIPS 2021 Data Centric AI Workshop2021 Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to evaluate the robustness of…
  • One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval

    Akari Asai, Xinyan Yu, Jungo Kasai, Hanna HajishirziNeurIPS2021 We present CORA, a Cross-lingual Open-Retrieval Answer Generation model that can answer questions across many languages even when language-specific annotated data or knowledge sources are unavailable. We introduce a new dense passage retrieval algorithm that…
  • Teach Me to Explain: A Review of Datasets for Explainable NLP

    Sarah Wiegreffe and Ana Marasović NeurIPS2021 Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models to produce…
  • LongChecker: Improving scientific claim verification by modeling full-abstract context

    David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, Hannaneh HajishirziarXiv2021 We introduce the LONGCHECKER system for scientific claim verification. Given a scientific claim and an evidence-containing research abstract, LONGCHECKER predicts a veracity label and identifies supporting rationales in a multitask fashion based on a shared…
  • Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

    Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. SmitharXiv2021 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…
  • Specializing Multilingual Language Models: An Empirical Study

    Ethan C. Chau, Noah A. SmithEMNLP • Workshop on Multilingual Representation Learning2021
    Best Paper Honorable Mention
    Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary…
  • CDLM: Cross-Document Language Modeling

    Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, Ido DaganFindings of EMNLP2021 We introduce a new pretraining approach for language models that are geared to support multi-document NLP tasks. Our crossdocument language model (CD-LM) improves masked language modeling for these tasks with two key ideas. First, we pretrain with multiple…