Ayush Pareek


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Measuring Bias in Contextualized Word Representations
Keita Kurita | Nidhi Vyas | Ayush Pareek | Alan W Black | Yulia Tsvetkov
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well. In this study, we (1) propose a template-based method to quantify bias in BERT; (2) show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3) conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution. Although our case study focuses on gender bias, the proposed technique is generalizable to unveiling other biases, including in multiclass settings, such as racial and religious biases.


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Graph-based Neural Multi-Document Summarization
Michihiro Yasunaga | Rui Zhang | Kshitijh Meelu | Ayush Pareek | Krishnan Srinivasan | Dragomir Radev
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences that avoid redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon other traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.


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A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques
Mukesh Kumar Jadon | Ayush Pareek
Proceedings of the 13th International Conference on Natural Language Processing