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Michele Bevilacqua

9 accepted papers

2023

Cross-Domain Image Captioning With Discriminative Finetuning

CVPR 2023poster

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discrimi…

2022

Autoregressive Search Engines: Generating Substrings as Document Identifiers

NeurIPS 2022accept

Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive language models are emerging as the de-facto standard for generating answers, with newer and more powerful systems emerging at an astoni…

2022

Nibbling at the Hard Core of Word Sense Disambiguation

ACL 2022long

With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained languag…

2021

Integrating Personalized PageRank into Neural Word Sense Disambiguation

EMNLP 2021main

Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neigh…

2021

One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline

AAAI 2021technical

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizab…

2021

Recent Trends in Word Sense Disambiguation: A Survey

IJCAI 2021poster

Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory. Recent breakthroughs in representation learning have fueled intensive WSD research, resulting in considerable performance improvement…

Cited by 183SourcePDFScholar
2021

SPRING Goes Online: End-to-End AMR Parsing and Generation

EMNLP 2021system demonstrations

In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by…

2021

Ten Years of BabelNet: A Survey

IJCAI 2021poster

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI. However, when it comes to Natural Language Processing (NLP), symbols have to be mapped to words and phrases, which are not only ambiguous but also language-specific: multilinguality is indeed a desirable property f…

Cited by 84SourcePDFScholar
2020

EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings

IJCAI 2020poster

The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-anno…

Cited by 0SourcePDFScholar