activity
20182023
most citedRecent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey

161 citations · 174 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2023

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…

cs.CL2022★ 4 cited

Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning

Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle +2

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few…

cs.CL2022★ 1 cited

ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations

Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle +2

The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this d…

cs.CL2022★ 1 cited

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

Minh Van Nguyen, Nghia Trung Ngo, Bonan Min +1

This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in…

cs.CL2021★ 161 cited

Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey

Bonan Min, Hayley Ross, Elior Sulem +6

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses…

cs.CL2021★ 1 cited

ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19

Bonan Min, Benjamin Rozonoyer, Haoling Qiu +2

Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are repor…

cs.CL2020★ 2 cited

Exploring Contextualized Neural Language Models for Temporal Dependency Parsing

Hayley Ross, Jonathon Cai, Bonan Min

Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challengin…

cs.CL2018★ 4 cited

Rapid Customization for Event Extraction

Yee Seng Chan, Joshua Fasching, Haoling Qiu +1

We present a system for rapidly customizing event extraction capability to find new event types and their arguments. The system allows a user to find, expand and filter event trigg…