Researcher profile

Jianmo Ni

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Promptagator: Few-shot Dense Retrieval From 8 Examples

    2022 · arXiv (Cornell University)

    Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is …

  2. HYRR: Hybrid Infused Reranking for Passage Retrieval

    2022 · arXiv (Cornell University)

    We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both …

  3. Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

    2019

    Liliang Ren, Jianmo Ni, Julian McAuley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

  4. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects

    2019

    Jianmo Ni, Jiacheng Li, Julian McAuley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

  5. Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

    2022 · Findings of the Association for Computational Linguistics: ACL 2022

    We provide the first exploration of sentence embeddings from text-to-text transformers (T5) including the effects of scaling up sentence encoders to 11B parameters. Sentence embeddings are broadly useful for language processing tasks. While T5 achieves …

  6. LongT5: Efficient Text-To-Text Transformer for Long Sequences

    2022 · Findings of the Association for Computational Linguistics: NAACL 2022

    Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present LongT5, a new model that explores …

  7. Large Dual Encoders Are Generalizable Retrievers

    2022

    Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. …