Simone Conia

Simone Conia
Sapienza University of Rome | la sapienza · Department of Computer Science

PhD student in Computer Science
PhD student in NLP and AI at the Sapienza NLP group. Currently working on multilingual and cross-lingual semantics.

About

21
Publications
4,735
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96
Citations
Introduction
3rd-year PhD student in Computer Science, member of the Sapienza NLP group. Currently studying Natural Language Processing (NLP), with a focus on Semantic Role Labeling (SRL), Word Sense Disambiguation (WSD) and Semantic Parsing.
Additional affiliations
November 2019 - present
Sapienza University of Rome
Position
  • PhD Student
Description
  • I am among those lucky people who can study what they love and love what they study. Currently, I am a second-year PhD student in Computer Science at the Sapienza NLP group. My research revolves around multilinguality and cross-linguality in Semantics, with a focus on Semantic Role Labeling, Word Sense Disambiguation and Semantic Parsing.
Education
September 2016 - January 2019
Sapienza University of Rome
Field of study
  • Engineering in Computer Science
September 2013 - October 2016
Sapienza University of Rome
Field of study
  • Engineering in Computer Science

Publications

Publications (21)
Conference Paper
Full-text available
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings...
Conference Paper
Full-text available
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In t...
Conference Paper
Full-text available
Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available. While recent work has attained growing success, results on gold multilingual benchmarks are still not easily comparable across languages, making it di...
Conference Paper
Full-text available
Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing , there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of an...
Conference Paper
Full-text available
Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency-and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim...
Conference Paper
Full-text available
In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing. Together, such annotations can be used to provide users with rich and dive...
Conference Paper
Full-text available
In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabil...
Conference Paper
Full-text available
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 language models. And yet, if we look below the surface of...
Conference Paper
Full-text available
Entity Linking (EL) systems have achieved impressive results on standard benchmarks, mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data — millions of labeled examples — to perform at their best, with training times that often exceed several d...
Poster
Full-text available
Entity Linking (EL) systems have achieved impressive results on standard benchmarks, mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data — millions of labeled examples — to perform at their best, with training times that often exceed several d...
Conference Paper
Full-text available
Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, makin...
Poster
Full-text available
Despite the recent great success of the sequence-to-sequence paradigm in Natural Language Processing, the majority of current studies in Semantic Role Labeling (SRL) still frame the problem as a sequence labeling task. In this paper we go against the flow and propose GSRL (Generating Senses and RoLes), the first sequence-to-sequence model for end-t...
Conference Paper
Full-text available
Despite the recent great success of the sequence-to-sequence paradigm in Natural Language Processing, the majority of current studies in Semantic Role Labeling (SRL) still frame the problem as a sequence labeling task. In this paper we go against the flow and propose GSRL (Generating Senses and RoLes), the first sequence-to-sequence model for end-t...
Conference Paper
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 for NLP systems, and one which enables the generali...
Poster
Full-text available
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for...
Conference Paper
Full-text available
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for...
Conference Paper
Full-text available
To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amoun...
Poster
Full-text available
To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amoun...
Conference Paper
Full-text available
Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource, VerbAtlas, and a state-of-the...
Conference Paper
Full-text available
We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative seman...

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Projects

Projects (3)
Project
The aim of the project is to enable the development of high-performing NER and Entity Linking systems in multiple languages, even when few training data or limited hardware resources are available.
Project
Abstract Meaning representation for English and cross-lingually as an interlingua.