Papers by Alfio Gliozzo
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez 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 Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Topic Transferable Table Question Answering (2021.emnlp-main)
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Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan
| Challenge: | 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. |
| Approach: | They propose a framework for TableQA that incorporates topic-specific vocabulary injection into BERT, a novel text-to-text transformer generator and a logical form re-ranker. |
| Outcome: | The proposed framework provides a reasonably good baseline for topic shift benchmarks. |
Learning Relational Representations by Analogy using Hierarchical Siamese Networks (N19-1)
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| Challenge: | Existing approaches to learn representations of relations by textual mentions require a large amount of examples for each relation to reach satisfactory performance. |
| Approach: | They propose a method to learn representations of relations expressed by their textual mentions by matching triples in knowledge bases with web-scale corpora through distant supervision. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on a relation extraction task. |
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering (2021.acl-short)
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Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
| Challenge: | Existing knowledge base question answering systems do not leverage the explicit semantic parse of the question text. |
| Approach: | They propose a transformer-based neural model that leverages the AMR semantic parse of a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on 4 popular benchmark datasets. |
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
Discovering Implicit Knowledge with Unary Relations (P18-1)
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| Challenge: | State-of-the-art relation extraction methods only recognize relationships between mentions of entity arguments stated explicitly in the text. |
| Approach: | They propose a method to identify relations between two entities using unary relations and a common deep learning based representation. |
| Outcome: | The proposed method outperforms state-of-the-art relation extraction technology on a web scale knowledge base population benchmark. |
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer (2020.acl-main)
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| Challenge: | Existing taxonomies are either entirely absent or missing. |
| Approach: | They propose a GNN-based cross-domain transfer framework for the taxonomy construction task. |
| Outcome: | The proposed framework improves on benchmark datasets from science and environment domains. |
KGI: An Integrated Framework for Knowledge Intensive Language Tasks (2022.emnlp-demos)
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Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya
| Challenge: | Existing state-of-the-art retrieval augmented generation models are not available for knowledge-intensive language tasks. |
| Approach: | They propose a retrieval augmented generation system that showcases the latest state-of-the-art retrieval models on knowledge-intensive language tasks. |
| Outcome: | The proposed system is based on the core of the KGI system. |
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. |
Automatic Taxonomy Induction and Expansion (D19-3)
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Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Sarthak Dash, Md. Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya
| Challenge: | Knowledge Graph Induction Service (KGIS) enables automatic taxonomy induction and human-in-the-loop curation. |
| Approach: | They describe the features of the Knowledge Graph Induction Service (KGIS) KGIS allows the user to semi-automatically curate and expand the induced taxonomies through a component called Smart SpreadSheet . |
| Outcome: | The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge graph induction system. |
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering (2021.acl-demo)
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| Challenge: | Existing systems that retrieve tables based on keyword queries and table contents often result in poor quality . a growing demand for natural language questions over tables to be used for QA . |
| Approach: | They propose an end-to-end transformer-based table question answering system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables. |
| Outcome: | The proposed system can retrieve relevant tables and locate the correct cells to answer questions. |
Open Knowledge Graphs Canonicalization using Variational Autoencoders (2021.emnlp-main)
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| Challenge: | Existing approaches to solve this problem generate embeddings for noun and relation phrases . ambiguous subject-relation-object triples are created by open knowledge graphs . |
| Approach: | They propose a model to learn both embeddings and cluster assignments in an end-to-end approach . they propose CUVA to be able to group noun and relation phrases using embeddable features . |
| Outcome: | The proposed model outperforms state-of-the-art methods over multiple benchmarks. |
Re2G: Retrieve, Rerank, Generate (2022.naacl-main)
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Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, Alfio Gliozzo
| Challenge: | Recent models such as RAG and REALM incorporate retrieval into conditional generation. |
| Approach: | They propose a method that combines retrieval and reranking into a BART-based sequence-to-sequence generation. |
| Outcome: | The proposed model combines retrieval and reranking into a BART-based sequence-to-sequence generation. |
Retrieval-Based Transformer for Table Augmentation (2023.findings-acl)
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| Challenge: | Data preparation is one of the most expensive and time-consuming steps when performing analytics or building machine learning models. |
| Approach: | They propose a retrieval augmented transformer model that is self-trained for table augmentation tasks. |
| Outcome: | The proposed model outperforms current state-of-the-art models on EntiTables and WebTables. |
Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)
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| Challenge: | Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI. |
| Approach: | They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
| Outcome: | The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard. |
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. |
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL (2025.findings-acl)
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Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh, Dharmashankar Subramanian, Sola Shirai, Alfio Gliozzo, Debarun Bhattacharjya
| Challenge: | Existing approaches to translate natural language queries into SQL statements are limited in their parametric knowledge of the database schemas. |
| Approach: | They propose to construct a knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. |
| Outcome: | The proposed approach outperforms baselines on multiple text-to-SQL datasets and shows that it is practical and reliable. |