Search Results for author: Alfio Gliozzo

Found 39 papers, 14 papers with code

Permutation Invariant Strategy Using Transformer Encoders for Table Understanding

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.

Column Type Annotation Entity Linking +4

KGI: An Integrated Framework for Knowledge Intensive Language Tasks

no code implementations8 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.

Fact Checking Open-Domain Question Answering +4

End-to-End Table Question Answering via Retrieval-Augmented Generation

no code implementations30 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.

Information Retrieval Question Answering +1

A Generative Model for Relation Extraction and Classification

no code implementations26 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.

Classification Knowledge Base Population +2

Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation

no code implementations14 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.

Keyphrase Generation Language Modelling

Topic Transferable Table Question Answering

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.

Question Answering Question Generation +1

CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering

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.

Question Answering Retrieval

CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering

1 code implementation8 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.

Question Answering Retrieval

Zero-shot Slot Filling with DPR and RAG

2 code implementations17 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.

Knowledge Base Population Knowledge Graphs +3

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

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.

Question Answering

Type Prediction Systems

no code implementations2 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.

Entity Disambiguation Knowledge Base Question Answering +1

Open Knowledge Graphs Canonicalization using Variational Autoencoders

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.

Knowledge Graphs

SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

1 code implementation1 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.

Knowledge Base Question Answering Type prediction

Hypernym Detection Using Strict Partial Order Networks

no code implementations23 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.

Frustratingly Easy Natural Question Answering

no code implementations11 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.

Data Augmentation Natural Questions +2

Span Selection Pre-training for Question Answering

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).

Language Modelling Memorization +3

Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation

no code implementations21 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.

Knowledge Base Completion Knowledge Graphs +1

Distributional Negative Sampling for Knowledge Base Completion

no code implementations16 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.

Knowledge Base Completion

Learning Relational Representations by Analogy using Hierarchical Siamese Networks

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.

Entity Embeddings Knowledge Base Population +2

Discovering Implicit Knowledge with Unary Relations

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.

Knowledge Base Population Natural Language Inference +1

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