Samuel R. Bowman
20 papers in the PaperMetrix corpus
Papers by this author
-
Training a Ranking Function for Open-Domain Question Answering
2018 · arXiv (Cornell University)
In recent years, there have been amazing advances in deep learning methods for machine reading. In machine reading, the machine reader has to extract the answer from the given ground truth paragraph. Recently, the state-of-the-art …
-
Discovering Language Model Behaviors with Model-Written Evaluations
2022 · arXiv (Cornell University)
As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data …
-
A large annotated corpus for learning natural language inference
2015 · arXiv (Cornell University)
Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has …
-
Generating Sentences from a Continuous Space
2016
The standard recurrent neural network language model (rnnlm) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an rnn-based variational …
-
A Fast Unified Model for Parsing and Sentence Understanding
2016
Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, Christopher Potts. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
-
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
2018
Adina Williams, Nikita Nangia, Samuel Bowman. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
-
Annotation Artifacts in Natural Language Inference Data
2018
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, Noah A. Smith. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short …
-
Do latent tree learning models identify meaningful structure in sentences?
2018 · Transactions of the Association for Computational Linguistics
Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without …
-
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
2018 · arXiv (Cornell University)
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is …
-
Neural Network Acceptability Judgments
2019 · Transactions of the Association for Computational Linguistics
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set …
-
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
2018 · arXiv (Cornell University)
Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks. By supplementing language model-style pretraining with further training on data-rich supervised tasks, such …
-
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
2019
Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, Tom McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick. Proceedings of the Eighth Joint Conference on Lexical and …
-
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
2019 · arXiv (Cornell University)
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, …
-
XNLI: Evaluating Cross-lingual Sentence Representations
2018 · arXiv (Cornell University)
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
-
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
2019
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman. …
-
Do Attention Heads in BERT Track Syntactic Dependencies?
2019 · arXiv (Cornell University)
We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations. We employ two methods---taking the maximum attention weight and computing the …
-
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (Electronic Resources)
2020 · Faculty Digital Archive (New York University Florence)
We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing …
-
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
2020 · arXiv (Cornell University)
While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a …
-
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
2020
Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.
-
What do you learn from context? Probing for sentence structure in\n contextualized word representations
2019 · arXiv (Cornell University)
Contextualized representation models such as ELMo (Peters et al., 2018a) and\nBERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a\ndiverse array of downstream NLP tasks. Building on recent token-level probing\nwork, we introduce a …