208 citations · 381 across the 13 of their papers we have counts for
6 papers · 1 filter
Amortized Noisy Channel Neural Machine Translation
Richard Yuanzhe Pang, He He, Kyunghyun Cho
Noisy channel models have been especially effective in neural machine translation (NMT). However, recent approaches like "beam search and rerank" (BSR) incur significant computatio…
QuALITY: Question Answering with Long Input Texts, Yes!
Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi +8
To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average lengt…
Meta-learning via Language Model In-context Tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha +2
The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples. To tackle this problem in NLP, we propose , which recasts…
Types of Out-of-Distribution Texts and How to Detect Them
Udit Arora, William Huang, He He
Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them.…
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
Faisal Ladhak, Esin Durmus, He He +2
Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear w…
An Investigation of the (In)effectiveness of Counterfactually Augmented Data
Nitish Joshi, He He
While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out…