5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
15 papers · 2 filters
Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study
Xiaoyi Zhang, Rodoniki Athanasiadou, Narges Razavian
Twitter data has been shown broadly applicable for public health surveillance. Previous public health studies based on Twitter data have largely relied on keyword-matching or topic…
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
Sébastien Jean, Ankur Bapna, Orhan Firat
Most neural machine translation systems still translate sentences in isolation. To make further progress, a promising line of research additionally considers the surrounding contex…
Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity
Katharina Kann
The relation between language and thought has occupied linguists for at least a century. Neo-Whorfianism, a weak version of the controversial Sapir-Whorf hypothesis, holds that our…
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
Katharina Kann
How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, w…
The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation
Richard Yuanzhe Pang
The difficulty of textual style transfer lies in the lack of parallel corpora. Numerous advances have been proposed for the unsupervised generation. However, significant problems r…
Towards Controllable and Personalized Review Generation
Pan Li, Alexander Tuzhilin
In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic info…
ELI5: Long Form Question Answering
Angela Fan, Yacine Jernite, Ethan Perez +3
We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K thre…
Simple Automatic Post-editing for Arabic-Japanese Machine Translation
Ella Noll, Mai Oudah, Nizar Habash
A common bottleneck for developing machine translation (MT) systems for some language pairs is the lack of direct parallel translation data sets, in general and in certain domains.…
Neural Image Captioning
Elaina Tan, Lakshay Sharma
In recent years, the biggest advances in major Computer Vision tasks, such as object recognition, handwritten-digit identification, facial recognition, and many others., have all c…
Natural Language Understanding with the Quora Question Pairs Dataset
Lakshay Sharma, Laura Graesser, Nikita Nangia +1
This paper explores the task Natural Language Understanding (NLU) by looking at duplicate question detection in the Quora dataset. We conducted extensive exploration of the dataset…
The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation
Mai Oudah, Amjad Almahairi, Nizar Habash
Neural networks have become the state-of-the-art approach for machine translation (MT) in many languages. While linguistically-motivated tokenization techniques were shown to have…
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
Yusu Qian, Urwa Muaz, Ben Zhang +1
Gender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach b…
Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark
Nikita Nangia, Samuel R. Bowman
The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at l…
What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen +8
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downst…
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
Alex Wang, Kyunghyun Cho
We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from B…