Giuseppe Serra


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Distilling the Evidence to Augment Fact Verification Models
Beatrice Portelli | Jason Zhao | Tal Schuster | Giuseppe Serra | Enrico Santus
Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)

The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.

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Keyphrase Generation with GANs in Low-Resources Scenarios
Giuseppe Lancioni | Saida S.Mohamed | Beatrice Portelli | Giuseppe Serra | Carlo Tasso
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document. Several past studies provided diverse approaches to generate Keyphrases for an input document. However, all of these approaches still need to be trained on very large datasets. In this paper, we introduce BeGanKP, a new conditional GAN model to address the problem of Keyphrase Generation in a low-resource scenario. Our main contribution relies in the Discriminator’s architecture: a new BERT-based module which is able to distinguish between the generated and humancurated KPs reliably. Its characteristics allow us to use it in a low-resource scenario, where only a small amount of training data are available, obtaining an efficient Generator. The resulting architecture achieves, on five public datasets, competitive results with respect to the state-of-the-art approaches, using less than 1% of the training data.


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Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Marco Passon | Marco Lippi | Giuseppe Serra | Carlo Tasso
Proceedings of the 5th Workshop on Argument Mining

Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.


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Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages
Marco Basaldella | Muhammad Helmy | Elisa Antolli | Mihai Horia Popescu | Giuseppe Serra | Carlo Tasso
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.