Search Results for author:
Found 28 papers, 11 papers with code
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis
To perform the experiments we introduce EusIE, an event extraction dataset for Basque, which follows the Multilingual Event Extraction dataset (MEE).
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
We hypothesize that this is because explicit spatial relations rarely appear in the image captions used to train these models.
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble.
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
Ranked #1 on
Zero-shot Named Entity Recognition (NER)
on HarveyNER
(using extra training data)
Do Multilingual Language Models Think Better in English?
In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models.
Common Sense Reasoning
Cross-Lingual Natural Language Inference
+6
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories
Language Models are the core for almost any Natural Language Processing system nowadays.
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.
Ranked #1 on
Event Argument Extraction
on WikiEvents
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples.
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters.
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ranked #10 on
Relation Extraction
on TACRED
Inferring spatial relations from textual descriptions of images
Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object.
Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation
To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption.
Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions
Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions.
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction
In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previous step to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension.
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations.
Evaluating Multimodal Representations on Sentence Similarity: vSTS, Visual Semantic Textual Similarity Dataset
In this paper we introduce vSTS, a new dataset for measuring textual similarity of sentences using multimodal information.
Improving Translation Selection with Supersenses
Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT).
Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models
Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question.
Supervised Hierarchical Classification for Student Answer Scoring
This paper describes a hierarchical system that predicts one label at a time for automated student response analysis.
Matching Cultural Heritage items to Wikipedia
Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information.
