no code implementations • Findings (NAACL) 2022 • Sarthak Dash, Sugato Bagchi, Nandana Mihindukulasooriya, Alfio Gliozzo
Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.
1 code implementation • ACL (InterNLP) 2021 • Michael Glass, Md Faisal Mahbub Chowdhury, Yu Deng, Ruchi Mahindru, Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Nandana Mihindukulasooriya
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach.
1 code implementation • 20 Jun 2023 • Michael Glass, Xueqing Wu, Ankita Rajaram Naik, Gaetano Rossiello, Alfio Gliozzo
In this paper, we introduce a novel approach toward automatic data wrangling in an attempt to alleviate the effort of end-users, e. g. data analysts, in structuring dynamic views from data lakes in the form of tabular data.
1 code implementation • NAACL 2022 • Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, Alfio Gliozzo
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
Ranked #1 on Open-Domain Question Answering on KILT: TriviaQA
no code implementations • 11 Jul 2022 • Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Irene Yachbes, Aditya Gidh, Jillian Duckwitz, Kovit Nisar, Michael Santos, Alfio Gliozzo
A research division plays an important role of driving innovation in an organization.
no code implementations • 8 Apr 2022 • Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking.
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.
no code implementations • 26 Feb 2022 • Jian Ni, Gaetano Rossiello, Alfio Gliozzo, Radu Florian
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
no code implementations • 14 Jan 2022 • Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass, Nandana Mihindukulasooriya, Alfio Gliozzo
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies.
no code implementations • 7 Dec 2021 • Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck, Gaetano Rossiello, Uttam Kumar
The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges.
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.
2 code implementations • EMNLP 2021 • Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
Ranked #1 on Zero-shot Slot Filling on T-REx
no code implementations • 16 Aug 2021 • Gaetano Rossiello, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Mihaela Bornea, Alfio Gliozzo, Tahira Naseem, Pavan Kapanipathi
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on Relation Linking on QALD-9
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.
no code implementations • ACL 2021 • Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
Relation linking is a crucial component of Knowledge Base Question Answering systems.
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.
2 code implementations • 17 Apr 2021 • Michael Glass, Gaetano Rossiello, Alfio Gliozzo
Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.
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.
1 code implementation • EMNLP 2021 • Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya, Sugato Bagchi, Alfio Gliozzo
In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.
1 code implementation • Findings (ACL) 2021 • Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
1 code implementation • 1 Dec 2020 • Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
1 code implementation • 16 Sep 2020 • Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Mo Yu, Alfio Gliozzo, Salim Roukos, Alexander Gray
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on Relation Linking on QALD-7
no code implementations • ACL 2020 • Chao Shang, Sarthak Dash, Md. Faisal Mahbub Chowdhury, N Mihindukulasooriya, ana, Alfio Gliozzo
However, there has been no attempt to exploit GNN to create taxonomies.
no code implementations • IJCNLP 2019 • Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Sarthak Dash, Md. Faisal Mahbub Chowdhury, N Mihindukulasooriya, ana
The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system.
no code implementations • 23 Sep 2019 • Sarthak Dash, Md. Faisal Mahbub Chowdhury, Alfio Gliozzo, Nandana Mihindukulasooriya, Nicolas Rodolfo Fauceglia
This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints.
no code implementations • 11 Sep 2019 • Lin Pan, Rishav Chakravarti, Anthony Ferritto, Michael Glass, Alfio Gliozzo, Salim Roukos, Radu Florian, Avirup Sil
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.
Ranked #5 on Question Answering on Natural Questions (long)
1 code implementation • ACL 2020 • Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avirup Sil
BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).
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 • 16 Aug 2019 • Sarthak Dash, Alfio Gliozzo
State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities.
no code implementations • NAACL 2019 • Gaetano Rossiello, Alfio Gliozzo, Robert Farrell, Nicolas Fauceglia, Michael Glass
We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions.
1 code implementation • ACL 2018 • Michael Glass, Alfio Gliozzo
State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence.
1 code implementation • European Semantic Web Conference 2018 • Michael Glass, Alfio Gliozzo
Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope.