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Found 19 papers, 4 papers with code
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect.
Revealing the Dark Secrets of BERT
BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success.
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives.
Similarity-Based Reconstruction Loss for Meaning Representation
This paper addresses the problem of representation learning.
Adversarial Decomposition of Text Representation
We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence.
Lessons from Natural Language Inference in the Clinical Domain
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian
This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages.
Ranked #2 on
Sentiment Analysis
on RuSentiment
Forced Apart: Discovering Disentangled Representations Without Exhaustive Labels
Learning a better representation with neural networks is a challenging problem, which has been tackled from different perspectives in the past few years.
Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013--2014
We show that on the task of predicting which side is likely to prefer a given article, a Naive Bayes classifier can record 90. 3{\%} accuracy looking only at domain names of the news sources.
HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition
This paper describes the winning system for SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor.
SemEval-2017 Task 6: \#HashtagWars: Learning a Sense of Humor
This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead.
Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels
Learning a better representation with neural networks is a challenging problem, which was tackled extensively from different prospectives in the past few years.
Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture
In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text.
Here's My Point: Joint Pointer Architecture for Argument Mining
One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text.
#HashtagWars: Learning a Sense of Humor
Our best supervised system achieved 63. 7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks.
Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
The goal of this paper is to develop evaluation methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions of this task.
