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Edoardo Barba

17 accepted papers

2025

Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation

NAACL 2025findings

The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not o…

2024

Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics

EMNLP 2024main

Machine Translation (MT) evaluation metrics assess translation quality automatically. Recently, researchers have employed MT metrics for various new use cases, such as data filtering and translation re-ranking. However, most MT metrics return assessments as scalar scores that are difficult to interp…

2024

Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!

ACL 2024long

Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments. Their results guide researchers toward enhancing the next generation of metr…

2024

MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus

NAACL 2024long

Several Natural Language Understanding (NLU) tasks focus on linking text to explicit knowledge, including Word Sense Disambiguation, Semantic Role Labeling, Semantic Parsing, and Relation Extraction. In addition to the importance of connecting raw text with explicit knowledge bases, the integration…

2024

Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends

ACL 2024long

Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to attain superior results has, at times, led to the premature replacement of carefully designed task-specific approaches w…

2024

ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

ACL 2024findings

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module…

2024

Word Sense Linking: Disambiguating Outside the Sandbox

ACL 2024findings

Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the ti…

2023

Code-Switching with Word Senses for Pretraining in Neural Machine Translation

EMNLP 2023long findings

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining paradigm of denoising synthetic "code-switched" text (…

Cited by 0SourceScholar
2022

STEPS: Semantic Typing of Event Processes with a Sequence-to-Sequence Approach

AAAI 2022technical

Enabling computers to comprehend the intent of human actions by processing language is one of the fundamental goals of Natural Language Understanding. An emerging task in this context is that of free-form event process typing, which aims at understanding the overall goal of a protagonist in terms…

2022

Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures

EMNLP 2022finding

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.However, we argue this need not be the case. In this paper, we present an appr…

2021

ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension

EMNLP 2021main

Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient. However, while these systems have certainly attained unprecedented performances, virtually all of them operate under the constraining a…

2021

Exemplification Modeling: Can You Give Me an Example, Please?

IJCAI 2021poster

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemp…

2021

MultiMirror: Neural Cross-lingual Word Alignment for Multilingual Word Sense Disambiguation

IJCAI 2021poster

Word Sense Disambiguation (WSD), i.e., the task of assigning senses to words in context, has seen a surge of interest with the advent of neural models and a considerable increase in performance up to 80% F1 in English. However, when considering other languages, the availability of training data is l…

2020

MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation

IJCAI 2020poster

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation sc…