Mike Lewis

Joint Verification and Reranking for Open Fact Checking Over Tables

2021

Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, Sebastian Riedel. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing …

LSTM CCG Parsing

2016

We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies.Instead, all dependencies are implicitly encoded in an LSTM supertagger …

Deep Semantic Role Labeling: What Works and What’s Next

2017

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway …

Hierarchical Neural Story Generation

2018 · arXiv (Cornell University)

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. …

Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

2019

Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case

2019 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the …

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

2020

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, Luke Zettlemoyer. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Multilingual Denoising Pre-training for Neural Machine Translation

2020 · Transactions of the Association for Computational Linguistics

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART—a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using …

Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems

2026 · arXiv (Cornell University)

Affordance-Compiled Intelligence develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for LLM-integrated systems. The paper studies how a fixed model-policy can exhibit different operational capability when the surrounding world is …

Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

2020

Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input. Existing automatic evaluation metrics for summarization are largely insensitive to such errors. We propose QAGS, 1 an automatic …

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

2022

Large language models (LMs) are able to in-context learn—perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding …

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

2023

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.