no code implementations • 30 Mar 2022 • Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, James Hendler
Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader to locate the correct answers from table candidates.
1 code implementation • EMNLP 2021 • Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan
Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question.
no code implementations • ACL 2021 • Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, Peter Fox
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
1 code implementation • NAACL (ACL) 2022 • Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL.
1 code implementation • 8 Jun 2021 • Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, Peter Fox
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpus as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
1 code implementation • NAACL 2021 • Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.
no code implementations • 2 Apr 2021 • Sarthak Dash, Nandana Mihindukulasooriya, Alfio Gliozzo, Mustafa Canim
Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc.
no code implementations • 21 Nov 2019 • Mustafa Canim, Cristina Cornelio, Arun Iyengar, Ryan Musa, Mariano Rodrigez Muro
Unstructured enterprise data such as reports, manuals and guidelines often contain tables.
no code implementations • 21 Aug 2019 • Sarthak Dash, Michael R. Glass, Alfio Gliozzo, Mustafa Canim
In addition to that, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations.
no code implementations • 8 Aug 2019 • Mustafa Canim, Ashish Kundu, Josh Payne
Given a classification service supplier $S$, intermediary CaaS provider $P$ claiming to use $S$ as a classification backend, and customer $C$, our work addresses the following questions: (i) how can $P$'s claim to be using $S$ be verified by $C$?