Search Results for author: Radu Florian

Found 51 papers, 8 papers with code

IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification

no code implementations ACL (CASE) 2021 Ken Barker, Parul Awasthy, Jian Ni, Radu Florian

The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types.

Classification Natural Language Inference +1

IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

no code implementations ACL (CASE) 2021 Parul Awasthy, Jian Ni, Ken Barker, Radu Florian

In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021.

Event Detection Language Modelling

Not to Overfit or Underfit? A Study of Domain Generalization in Question Answering

no code implementations15 May 2022 Md Arafat Sultan, Avirup Sil, Radu Florian

Machine learning models are prone to overfitting their source (training) distributions, which is commonly believed to be why they falter in novel target domains.

Domain Generalization Knowledge Distillation +2

Inducing and Using Alignments for Transition-based AMR Parsing

1 code implementation3 May 2022 Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramon Fernandez Astudillo

These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.

AMR Parsing

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

DocAMR: Multi-Sentence AMR Representation and Evaluation

1 code implementation15 Dec 2021 Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider

Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.

Coreference Resolution

Learning to Transpile AMR into SPARQL

no code implementations15 Dec 2021 Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Pavan Kapanipathi, Radu Florian, Salim Roukos

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).

Knowledge Base Question Answering Semantic Parsing

Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

1 code implementation14 Dec 2021 Young-suk Lee, Ramon Fernandez Astudillo, Thanh Lam Hoang, Tahira Naseem, Radu Florian, Salim Roukos

AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.

 Ranked #1 on AMR Parsing on LDC2017T10 (using extra training data)

AMR Parsing Data Augmentation +3

Do Answers to Boolean Questions Need Explanations? Yes

no code implementations14 Dec 2021 Sara Rosenthal, Mihaela Bornea, Avirup Sil, Radu Florian, Scott McCarley

Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question.

Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

no code implementations EMNLP 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-suk Lee, Radu Florian, Salim Roukos

We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.

Ranked #7 on AMR Parsing on LDC2017T10 (using extra training data)

AMR Parsing

VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension

no code implementations ACL 2021 Haoyang Wen, Anthony Ferritto, Heng Ji, Radu Florian, Avirup Sil

Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.

Machine Reading Comprehension

AMR Parsing with Action-Pointer Transformer

1 code implementation NAACL 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Radu Florian

In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.

Ranked #9 on AMR Parsing on LDC2020T02 (using extra training data)

AMR Parsing Hard Attention

Bootstrapping Multilingual AMR with Contextual Word Alignments

no code implementations EACL 2021 Janaki Sheth, Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.

Multilingual Word Embeddings Word Alignment

Multilingual Transfer Learning for QA Using Translation as Data Augmentation

no code implementations10 Dec 2020 Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian, Avirup Sil

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.

14 Cross-Lingual Transfer +5

End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

no code implementations2 Dec 2020 Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan, Rong Zhang, Avi Sil, Vittorio Castelli, Radu Florian, Salim Roukos

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.

Domain Adaptation Information Retrieval +2

Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers

no code implementations COLING 2020 Yousef El-Kurdi, Hiroshi Kanayama, Efsun Sarioglu Kayi, Vittorio Castelli, Todd Ward, Radu Florian

We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text.

Data Augmentation

Towards building a Robust Industry-scale Question Answering System

no code implementations COLING 2020 Rishav Chakravarti, Anthony Ferritto, Bhavani Iyer, Lin Pan, Radu Florian, Salim Roukos, Avi Sil

Building on top of the powerful BERTQA model, GAAMA provides a ∼2. 0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ.

Data Augmentation Question Answering +1

Pushing the Limits of AMR Parsing with Self-Learning

no code implementations Findings of the Association for Computational Linguistics 2020 Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.

 Ranked #1 on AMR Parsing on LDC2014T12 (F1 Full metric)

AMR Parsing Machine Translation +4

Cross-Lingual Relation Extraction with Transformers

no code implementations16 Oct 2020 Jian Ni, Taesun Moon, Parul Awasthy, Radu Florian

Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications.

Cross-Lingual Transfer Relation Extraction +1

ARES: A Reading Comprehension Ensembling Service

no code implementations EMNLP 2020 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil

We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2. 3 points.

Machine Reading Comprehension Question Answering

Cascaded Models for Better Fine-Grained Named Entity Recognition

no code implementations15 Sep 2020 Parul Awasthy, Taesun Moon, Jian Ni, Radu Florian

Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction.

Named Entity Recognition NER +1

Towards Lingua Franca Named Entity Recognition with BERT

no code implementations19 Nov 2019 Taesun Moon, Parul Awasthy, Jian Ni, Radu Florian

In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.

Cross-Lingual NER Named Entity Recognition

Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping

no code implementations IJCNLP 2019 Jian Ni, Radu Florian

Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications.

Relation Extraction Word Embeddings

Ensembling Strategies for Answering Natural Questions

no code implementations30 Oct 2019 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avirup Sil

Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) challenges.

Question Answering

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

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

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

no code implementations ACL 2019 Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.

AMR Parsing reinforcement-learning

Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks

no code implementations6 Sep 2018 Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question.

Multi-Hop Reading Comprehension Question Answering

Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking

no code implementations ACL 2018 Gourab Kundu, Avirup Sil, Radu Florian, Wael Hamza

We propose an entity-centric neural cross-lingual coreference model that builds on multi-lingual embeddings and language-independent features.

Coreference Resolution Entity Linking

Neural Cross-Lingual Entity Linking

no code implementations5 Dec 2017 Avirup Sil, Gourab Kundu, Radu Florian, Wael Hamza

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts.

Cross-Lingual Entity Linking Entity Disambiguation +3

One for All: Towards Language Independent Named Entity Linking

no code implementations ACL 2016 Avirup Sil, Radu Florian

Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc).

14 Entity Linking

Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping

no code implementations EMNLP 2016 Jian Ni, Radu Florian

Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18. 3 F1 score improvement).

Multilingual Named Entity Recognition NER

Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures

no code implementations EMNLP 2017 Lifu Huang, Avirup Sil, Heng Ji, Radu Florian

Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents.

Relation Extraction Slot Filling

Reinforcement Learning for Transition-Based Mention Detection

no code implementations13 Mar 2017 Georgiana Dinu, Wael Hamza, Radu Florian

This paper describes an application of reinforcement learning to the mention detection task.

reinforcement-learning

Multi-Perspective Context Matching for Machine Comprehension

1 code implementation13 Dec 2016 Zhiguo Wang, Haitao Mi, Wael Hamza, Radu Florian

Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.

Question Answering Reading Comprehension

Toward Mention Detection Robustness with Recurrent Neural Networks

no code implementations24 Feb 2016 Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, Radu Florian

One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages.

Named Entity Recognition Word Embeddings

A Joint Model for Answer Sentence Ranking and Answer Extraction

no code implementations TACL 2016 Md. Arafat Sultan, Vittorio Castelli, Radu Florian

Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i. e., as independent tasks.

Information Retrieval Question Answering +1

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