Search Results for author: Michael Glass

Found 16 papers, 9 papers with code

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

AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

1 code implementation24 Jun 2021 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.

Question Answering Semantic Parsing

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

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 +1

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

Scalable Hierarchical Clustering with Tree Grafting

1 code implementation31 Dec 2019 Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum

We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets.

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 Question Answering +1

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 Language understanding +2

CFO: A Framework for Building Production NLP Systems

no code implementations IJCNLP 2019 Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avirup Sil

This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.

Information Retrieval Machine Reading Comprehension +1

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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