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Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons
Akash Kumar Mohankumar, Mitesh M. Khapra
Recent studies have shown the advantages of evaluating NLG systems using pairwise comparisons as opposed to direct assessment. Given systems, a naive approach for identifying t…
Diversity driven Query Rewriting in Search Advertising
Akash Kumar Mohankumar, Nikit Begwani, Amit Singh
Retrieving keywords (bidwords) with the same intent as query, referred to as close variant keywords, is of prime importance for effective targeted search advertising. For head and…
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining
Ananya B. Sai, Akash Kumar Mohankumar, Siddhartha Arora +1
There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all re…
A Survey of Evaluation Metrics Used for NLG Systems
Ananya B. Sai, Akash Kumar Mohankumar, Mitesh M. Khapra
The success of Deep Learning has created a surge in interest in a wide a range of Natural Language Generation (NLG) tasks. Deep Learning has not only pushed the state of the art in…
Towards Transparent and Explainable Attention Models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan +3
Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model's predictions. Attention distributions can be c…
Let's Ask Again: Refine Network for Automatic Question Generation
Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra +2
In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that…