4 citations · 9 across the 11 of their papers we have counts for
14 papers
ReTAG: Reasoning Aware Table to Analytic Text Generation
Deepanway Ghosal, Preksha Nema, Aravindan Raghuveer
The task of table summarization involves generating text that both succinctly and accurately represents the table or a specific set of highlighted cells within a table. While signi…
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
Anubhav Jangra, Preksha Nema, Aravindan Raghuveer
Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content…
A Framework for Rationale Extraction for Deep QA models
Sahana Ramnath, Preksha Nema, Deep Sahni +1
As neural-network-based QA models become deeper and more complex, there is a demand for robust frameworks which can access a model's rationale for its prediction. Current technique…
The heads hypothesis: A unifying statistical approach towards understanding multi-headed attention in BERT
Madhura Pande, Aakriti Budhraja, Preksha Nema +2
Multi-headed attention heads are a mainstay in transformer-based models. Different methods have been proposed to classify the role of each attention head based on the relations bet…
Towards Interpreting BERT for Reading Comprehension Based QA
Sahana Ramnath, Preksha Nema, Deep Sahni +1
BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being capt…
On the Importance of Local Information in Transformer Based Models
Madhura Pande, Aakriti Budhraja, Preksha Nema +2
The self-attention module is a key component of Transformer-based models, wherein each token pays attention to every other token. Recent studies have shown that these heads exhibit…
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…
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples
Soham Parikh, Ananya B. Sai, Preksha Nema +1
When humans learn to perform a difficult task (say, reading comprehension (RC) over longer passages), it is typically the case that their performance improves significantly on an e…
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions
Soham Parikh, Ananya B. Sai, Preksha Nema +1
The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given passage, question pair and select one of the n given options. The cu…
Towards a Better Metric for Evaluating Question Generation Systems
Preksha Nema, Mitesh M. Khapra
There has always been criticism for using -gram based similarity metrics, such as BLEU, NIST, etc, for evaluating the performance of NLG systems. However, these metrics continue…
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Parag Jain, Anirban Laha, Karthik Sankaranarayanan +3
Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the f…
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
Preksha Nema, Shreyas Shetty, Parag Jain +3
In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values…
Diversity driven Attention Model for Query-based Abstractive Summarization
Preksha Nema, Mitesh Khapra, Anirban Laha +1
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summa…