b'Salim Roukos'

research

∙ 06/18/2023

MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types

With the growing interest in large language models, the need for evaluat...

6 Keerthiram Murugesan, et al. ∙

research

∙ 05/31/2023

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency

We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a s...

0 Maxwell Crouse, et al. ∙

research

∙ 05/26/2023

Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing

The sliding window approach provides an elegant way to handle contexts o...

0 Sadhana Kumaravel, et al. ∙

research

∙ 04/24/2023

AMR Parsing with Instruction Fine-tuned Pre-trained Language Models

Instruction fine-tuned language models on a collection of instruction an...

0 Young-suk Lee, et al. ∙

research

∙ 03/01/2023

UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers

Many information retrieval tasks require large labeled datasets for fine...

0 Jon Saad-Falcon, et al. ∙

research

∙ 01/23/2023

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

The field of Question Answering (QA) has made remarkable progress in rec...

0 Avirup Sil, et al. ∙

research

∙ 12/02/2022

Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking

Neural information retrieval (IR) systems have progressed rapidly in rec...

0 Keshav Santhanam, et al. ∙

research

∙ 10/15/2022

A Closer Look at the Calibration of Differentially Private Learners

We systematically study the calibration of classifiers trained with diff...

0 Hanlin Zhang, et al. ∙

research

∙ 04/20/2022

Synthetic Target Domain Supervision for Open Retrieval QA

Neural passage retrieval is a new and promising approach in open retriev...

0 Revanth Gangi Reddy, et al. ∙

research

∙ 01/15/2022

A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

Knowledge Base Question Answering (KBQA) tasks that involve complex reas...

1 Sumit Neelam, et al. ∙

research

∙ 12/15/2021

DocAMR: Multi-Sentence AMR Representation and Evaluation

Despite extensive research on parsing of English sentences into Abstract...

0 Tahira Naseem, et al. ∙

research

∙ 12/15/2021

Learning to Transpile AMR into SPARQL

We propose a transition-based system to transpile Abstract Meaning Repre...

8 Mihaela Bornea, et al. ∙

research

∙ 12/14/2021

Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

AMR parsing has experienced an unprecendented increase in performance in...

0 Young-suk Lee, et al. ∙

research

∙ 10/29/2021

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

Predicting linearized Abstract Meaning Representation (AMR) graphs using...

0 Jiawei Zhou, et al. ∙

research

∙ 09/28/2021

SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases

Knowledge Base Question Answering (KBQA) tasks that in-volve complex rea...

4 Sumit Neelam, et al. ∙

research

∙ 09/16/2021

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

Recent interest in Knowledge Base Completion (KBC) has led to a plethora...

36 Prithviraj Sen, et al. ∙

research

∙ 02/03/2021

Bootstrapping Multilingual AMR with Contextual Word Alignments

We develop high performance multilingualAbstract Meaning Representation ...

0 Janaki Sheth, et al. ∙

research

∙ 12/03/2020

Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning

Knowledge base question answering (KBQA) is an important task in Natural...

3 Pavan Kapanipathi, et al. ∙

research

∙ 12/02/2020

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

End-to-end question answering (QA) requires both information retrieval (...

0 Revanth Gangi Reddy, et al. ∙

research

∙ 10/20/2020

Pushing the Limits of AMR Parsing with Self-Learning

Abstract Meaning Representation (AMR) parsing has experienced a notable ...

0 Young-suk Lee, et al. ∙

research

∙ 10/12/2020

Multi-Stage Pre-training for Low-Resource Domain Adaptation

Transfer learning techniques are particularly useful in NLP tasks where ...

0 Rong Zhang, et al. ∙

research

∙ 09/16/2020

Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

Knowledgebase question answering systems are heavily dependent on relati...

0 Nandana Mihindukulasooriya, et al. ∙

research

∙ 05/18/2020

GPT-too: A language-model-first approach for AMR-to-text generation

Abstract Meaning Representations (AMRs) are broad-coverage sentence-leve...

0 Manuel Mager, et al. ∙

research

∙ 11/08/2019

The TechQA Dataset

We introduce TechQA, a domain-adaptation question answering dataset for ...

0 Vittorio Castelli, et al. ∙

research

∙ 11/05/2019

Heuristics for Interpretable Knowledge Graph Contextualization

In this paper, we introduce the problem of knowledge graph contextualiza...

0 Kshitij Fadnis, et al. ∙

research

∙ 10/30/2019

Ensembling Strategies for Answering Natural Questions

Many of the top question answering systems today utilize ensembling to i...

0 Anthony Ferritto, et al. ∙

research

∙ 09/11/2019

Frustratingly Easy Natural Question Answering

Existing literature on Question Answering (QA) mostly focuses on algorit...

0 Lin Pan, et al. ∙

research

∙ 08/16/2019

CFO: A Framework for Building Production NLP Systems

This paper introduces a novel orchestration framework, called CFO (COMPU...

0 Rishav Chakravarti, et al. ∙

research

∙ 05/31/2019

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Our work involves enriching the Stack-LSTM transition-based AMR parser (...

0 Tahira Naseem, et al. ∙

research

∙ 11/10/2009

Active Learning for Mention Detection: A Comparison of Sentence Selection Strategies

We propose and compare various sentence selection strategies for active ...

0 Nitin Madnani, et al. ∙

Salim Roukos

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