984 citations · 3.2k across the 49 of their papers we have counts for
8 papers · 2 filters
The Lifted Matrix-Space Model for Semantic Composition
WooJin Chung, Sheng-Fu Wang, Samuel R. Bowman
Tree-structured neural network architectures for sentence encoding draw inspiration from the approach to semantic composition generally seen in formal linguistics, and have shown e…
Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov, Samuel R. Bowman
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 se…
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nikita Nangia, Adina Williams, Angeliki Lazaridou +1
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inferenc…
Sequential Attention: A Context-Aware Alignment Function for Machine Reading
Sebastian Brarda, Philip Yeres, Samuel R. Bowman
In this paper we propose a neural network model with a novel Sequential Attention layer that extends soft attention by assigning weights to words in an input sequence in a way that…
Detecting and Explaining Crisis
Rohan Kshirsagar, Robert Morris, Sam Bowman
Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text a…
Ruminating Reader: Reasoning with Gated Multi-Hop Attention
Yichen Gong, Samuel R. Bowman
To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-…
Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning
Yacine Jernite, Samuel R. Bowman, David Sontag
This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to tra…
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Adina Williams, Nikita Nangia, Samuel R. Bowman
This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sent…