3.1k citations · 5.5k across the 54 of their papers we have counts for
9 papers · 2 filters
Shortformer: Better Language Modeling using Shorter Inputs
Ofir Press, Noah A. Smith, Mike Lewis
Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexit…
Joint Verification and Reranking for Open Fact Checking Over Tables
Michael Schlichtkrull, Vladimir Karpukhin, Barlas Oğuz +3
Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on…
Nearest Neighbor Machine Translation
Urvashi Khandelwal, Angela Fan, Dan Jurafsky +2
We introduce -nearest-neighbor machine translation (NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using represen…
Conversational Semantic Parsing
Armen Aghajanyan, Jean Maillard, Akshat Shrivastava +8
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the repr…
Grounded Adaptation for Zero-shot Executable Semantic Parsing
Victor Zhong, Mike Lewis, Sida I. Wang +1
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines…
Pre-training via Paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh +3
We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the…
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus +9
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. Howe…
Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
Alex Wang, Kyunghyun Cho, Mike Lewis
Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input. Existing automatic evaluation metrics for su…
Multilingual Denoising Pre-training for Neural Machine Translation
Yinhan Liu, Jiatao Gu, Naman Goyal +5
This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART --…