SOCIOFILLMORE: A Tool for Discovering Perspectives

Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as "animals should have lawful rights", and these responses form a spectrum of perspect…

Discovering Latent Concepts Learned in BERT

A large number of studies that analyze deep neural network models and their ability to encode various linguistic and non-linguistic concepts provide an interpretation of the inner mechanics of these models. The scope of the analyses is limited to pre-defined concepts that reinforce the traditional linguistic knowledge and do not reflect on how novel concepts are learned by the model. We address this limitation by discovering and analyzing latent concepts learned in neural network models in an unsupervised fashion and provide interpretations from the model's perspective. In this work, we study:…

Discovering Representation Sprachbund For Multilingual Pre-Training

Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low resource ones. However, due to significant typological differences and contradictions between some languages, such models usually perform poorly on many languages and cross-lingual settings, which shows the difficulty of learning a single model to handle massive diverse languages well at the same time. To alleviate this issue, we present a new multilingual pre-training pipeline. We propose to generate language repres…

在心理量表中发现语义潜在结构：一种无响应路径的高效简化方法

Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification

本文提出一种基于语义结构的无响应方法，通过主题建模简化心理量表，实现60.5%的长度缩减并保持心理测量有效性。