61 citations · 112 across the 13 of their papers we have counts for
7 papers · 1 filter
Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg +2
In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on t…
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski +1
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM)…
Knowledge Transfer from Answer Ranking to Answer Generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg +2
Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (t…
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering
Siddhant Garg, Alessandro Moschitti
In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is…
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer
Siddhant Garg, Rohit Kumar Sharma, Yingyu Liang
Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. In this paper we show that concatenating the embeddings from the pre-tr…
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
Siddhant Garg, Thuy Vu, Alessandro Moschitti
We propose TANDA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model…