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Researcher profile

Jonathan Berant

16 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Memory Augmented Policy Optimization for Program Synthesis with Generalization

    2018 · arXiv (Cornell University)

    This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. …

  2. Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

    2019

    Mor Geva, Yoav Goldberg, Jonathan Berant. 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.

  3. A Simple Global Neural Discourse Parser

    2020 · arXiv (Cornell University)

    Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a simple chart-based neural discourse parser that does not …

  4. Inferring Implicit Relations in Complex Questions with Language Models

    2022 · arXiv (Cornell University)

    A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly. In this work, …

  5. Learning To Retrieve Prompts for In-Context Learning

    2021 · arXiv (Cornell University)

    In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without …

  6. Building a Semantic Parser Overnight

    2015

    How do we build a semantic parser in a new domain starting with zero training ex-amples? We introduce a new methodol-ogy for this setting: First, we use a simple grammar to generate logical forms paired …

  7. Imitation Learning of Agenda-based Semantic Parsers

    2015 · Transactions of the Association for Computational Linguistics

    Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train …

  8. Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

    2017

    Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic …

  9. Learning Recurrent Span Representations for Extractive Question Answering

    2016 · arXiv (Cornell University)

    The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of …

  10. Coarse-to-Fine Question Answering for Long Documents

    2017

    Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

  11. CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

    2018 · arXiv (Cornell University)

    When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very …

  12. Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

    2016 · arXiv (Cornell University)

    Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic …

  13. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

    2019

    Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In …

  14. oLMpics-On What Language Model Pre-training Captures

    2020 · Transactions of the Association for Computational Linguistics

    Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and …

  15. Explaining Question Answering Models through Text Generation

    2020 · arXiv (Cornell University)

    Large pre-trained language models (LMs) have been shown to perform surprisingly well when fine-tuned on tasks that require commonsense and world knowledge. However, in end-to-end architectures, it is difficult to explain what is the knowledge …

  16. Learning To Retrieve Prompts for In-Context Learning

    2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

    In-context learning is a recent paradigm in natural language understanding, where a large pretrained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without …