Papers by Rishav Chakravarti
A Multilingual Reading Comprehension System for more than 100 Languages (2020.coling-demos)
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Anthony Ferritto, Sara Rosenthal, Mihaela Bornea, Kazi Hasan, Rishav Chakravarti, Salim Roukos, Radu Florian, Avi Sil
| Challenge: | Recent advances in open domain question answering (QA) have focused on machine reading comprehension (MRC) |
| Approach: | They propose a multilingual machine reading comprehension (MRC) demo which can answer questions in over 100 languages. |
| Outcome: | The proposed system can answer questions in over 100 languages and integrates with IBM Watson's machine translation widget to improve language accessibility. |
The TechQA Dataset (2020.acl-main)
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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
| Challenge: | TECHQA is a domain-adaptation question answering dataset for the technical support domain. |
| Approach: | They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum . |
| Outcome: | The TECHQA dataset highlights two real-world issues from the automated customer support domain. |
Towards building a Robust Industry-scale Question Answering System (2020.coling-industry)
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| Challenge: | Existing systems that use “zero-shot transfer learning” (ZSTL) are difficult to train and have observation biases. |
| Approach: | They propose a production model called GAAMA which has two characteristics . it is robust and efficient, and trains on the recently introduced Natural Questions dataset . |
| Outcome: | The proposed model performs on two benchmarks: BioASQ and CovidQA. |
CFO: A Framework for Building Production NLP Systems (D19-3)
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Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avi Sil
| Challenge: | Using a new orchestration framework, we build, test, and deploy interactive NLP and IR systems to production environments. |
| Approach: | They introduce a new orchestration framework for building, experimenting with, and deploying interactive NLP and IR systems to production environments. |
| Outcome: | The proposed framework is well suited to a variety of use cases but is not suitable for academic benchmarking or industry specific use cases. |
ARES: A Reading Comprehension Ensembling Service (2020.emnlp-demos)
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Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil
| Challenge: | ARES is a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Approach: | They propose a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Outcome: | The proposed system increases F1 by 2.3 points on a short answer task using an ensemble of models. |
Span Selection Pre-training for Question Answering (2020.acl-main)
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Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avi Sil
| Challenge: | Pre-trained BERTs provide large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). |
| Approach: | They propose a new pre-training task inspired by reading comprehension to better align the pre- training from memorization to understanding. |
| Outcome: | The proposed model outperforms BERT-BASE and BERT LARGE on a new dataset and improves answer prediction F1 by 4 points and supporting fact prediction F1. |
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables (2021.naacl-main)
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Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
| Challenge: | Existing transformer based approaches have been used to answer questions over tables. |
| Approach: | They propose a transformer based architecture that independently classifies rows and columns to identify relevant cells and a model that incorporates existing tables to improve efficiency. |
| Outcome: | The proposed model outperforms the state-of-the-art transformer based approaches on WikiSQL lookup questions and achieves 3.4% and 18.86% additional precision improvement on the standard WikisQL benchmark. |