Search Results for author: Salim Roukos

Found 22 papers, 4 papers with code

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.

AMR Parsing Fine-tuning

Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion

no code implementations16 Sep 2021 Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Francois Luus, Salim Roukos, Alexander Gray

Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.

Inductive logic programming Knowledge Base Completion

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

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

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 +3

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

Path-Based Contextualization of Knowledge Graphs for Textual Entailment

no code implementations5 Nov 2019 Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.

Knowledge Graphs Natural Language Inference

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

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