161 citations · 178 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka +2
Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simp…
Ask2Transformers: Zero-Shot Domain labelling with Pre-trained Language Models
Oscar Sainz, German Rigau
In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, t…