22 research outputs found
Five sources of bias in natural language processing
Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures
Topological Sort for Sentence Ordering
Sentence ordering is the task of arranging the sentences of a given text in
the correct order. Recent work using deep neural networks for this task has
framed it as a sequence prediction problem. In this paper, we propose a new
framing of this task as a constraint solving problem and introduce a new
technique to solve it. Additionally, we propose a human evaluation for this
task. The results on both automatic and human metrics across four different
datasets show that this new technique is better at capturing coherence in
documents.Comment: Will be published at the Proceedings of the 58th Annual Meeting of
the Association for Computational Linguistics (ACL) 202
Linguistic Markers of Influence in Informal Interactions
There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for
Computational Linguistics) 201
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models
Pretrained large language models have become indispensable for solving
various natural language processing (NLP) tasks. However, safely deploying them
in real world applications is challenging because they generate toxic content.
To address this challenge, we propose two novel pretraining data augmentation
strategies that significantly reduce model toxicity without compromising its
utility. Our two strategies are: (1) MEDA: adds raw toxicity score as meta-data
to the pretraining samples, and (2) INST: adds instructions to those samples
indicating their toxicity. Our results indicate that our best performing
strategy (INST) substantially reduces the toxicity probability up to 61% while
preserving the accuracy on five benchmark NLP tasks as well as improving AUC
scores on four bias detection tasks by 1.3%. We also demonstrate the
generalizability of our techniques by scaling the number of training samples
and the number of model parameters.Comment: This paper will be presented at EACL 202