984 citations · 3.2k across the 49 of their papers we have counts for
12 papers · 1 filter
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia +13
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pre…
Verb Argument Structure Alternations in Word and Sentence Embeddings
Katharina Kann, Alex Warstadt, Adina Williams +1
Verbs occur in different syntactic environments, or frames. We investigate whether artificial neural networks encode grammatical distinctions necessary for inferring the idiosyncra…
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
Jason Phang, Thibault Févry, Samuel R. Bowman
Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks. By supplementing…
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau, Guillaume Lample, Ruty Rinott +4
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a si…
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
Kelly W. Zhang, Samuel R. Bowman
Recent work using auxiliary prediction task classifiers to investigate the properties of LSTM representations has begun to shed light on why pretrained representations, like ELMo (…
Grammar Induction with Neural Language Models: An Unusual Replication
Phu Mon Htut, Kyunghyun Cho, Samuel R. Bowman
A substantial thread of recent work on latent tree learning has attempted to develop neural network models with parse-valued latent variables and train them on non-parsing tasks, i…
Neural Network Acceptability Judgments
Alex Warstadt, Amanpreet Singh, Samuel R. Bowman
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 int…
ListOps: A Diagnostic Dataset for Latent Tree Learning
Nikita Nangia, Samuel R. Bowman
Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown…
Annotation Artifacts in Natural Language Inference Data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy +3
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses)…
Training a Ranking Function for Open-Domain Question Answering
Phu Mon Htut, Samuel R. Bowman, Kyunghyun Cho
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 grou…
A Stable and Effective Learning Strategy for Trainable Greedy Decoding
Yun Chen, Victor O. K. Li, Kyunghyun Cho +1
Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation. However…
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang, Amanpreet Singh, Julian Michael +3
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 lan…