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1
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus ...
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Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement ...
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3
Fine-Grained Fairness Analysis of Abusive Language Detection Systems with CheckList ...
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4
FrameNet-like Annotation of Olfactory Information in Texts ...
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5
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus ...
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6
A Smell is Worth a Thousand Words: Olfactory Information Extraction and Semantic Processing in a Multilingual Perspective (Invited Talk) ...
Tonelli, Sara. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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7
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection
In: EMNLP 2020 - Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-02972203 ; EMNLP 2020 - Conference on Empirical Methods in Natural Language Processing, Nov 2020, Virtual, France (2020)
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8
A Multilingual Evaluation for Online Hate Speech Detection
In: ISSN: 1533-5399 ; ACM Transactions on Internet Technology ; https://hal.archives-ouvertes.fr/hal-02972184 ; ACM Transactions on Internet Technology, Association for Computing Machinery, 2020, 20 (2), pp.1-22. ⟨10.1145/3377323⟩ (2020)
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9
Creating a Multimodal Dataset of Images and Text to Study Abusive Language ...
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10
Cross-Platform Evaluation for Italian Hate Speech Detection
In: CLiC-it 2019 - 6th Annual Conference of the Italian Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02381152 ; CLiC-it 2019 - 6th Annual Conference of the Italian Association for Computational Linguistics, Nov 2019, Bari, Italy (2019)
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11
Enhancing statistical machine translation with bilingual terminology in a CAT environment
Arcan, Mihael; Turchi, Marco; Tonelli, Sara; Buitelaar, Paul. - : Association for Machine Translation in the Americas, 2019
Abstract: In this paper, we address the problem of extracting and integrating bilingual terminology into a Statistical Machine Translation (SMT) system for a Computer Aided Translation (CAT) tool scenario. We develop a framework that, taking as input a small amount of parallel in-domain data, gathers domain-specific bilingual terms and injects them in an SMT system to enhance the translation productivity. Therefore, we investigate several strategies to extract and align bilingual terminology, and to embed it into the SMT. We compare two embedding methods that can be easily used at run-time without altering the normal activity of an SMT system: XML markup and the cache-based model. We tested our framework on two different domains showing improvements up to 15% BLEU score points. ; We would like to thank Dr. Ahmet Aker and Marcis Pinnis for providing us with their newest ¯ software and the technical support for it. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 and by the European Union supported project MateCat (ICT-2011.4.2- 287688). ; non-peer-reviewed
Keyword: Bilingual terminology; CAT environment; Machine translation
URL: http://hdl.handle.net/10379/14924
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12
SIMPITIKI corpus for simplification in Italian ...
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13
SIMPITIKI corpus for simplification in Italian ...
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14
SIMPITIKI corpus for simplification in Italian ...
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15
The impact of phrases on Italian lexical simplification ...
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16
Italian Lexical Simplification Benchmark ...
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17
The impact of phrases on Italian lexical simplification ...
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18
Italian Lexical Simplification Benchmark ...
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19
Never Retreat, Never Retract: Argumentation Analysis for Political Speeches
In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence ; AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-01876442 ; AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence, Feb 2018, New Orleans, United States. pp.4889-4896 ; https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16393 (2018)
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20
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
In: http://hw.oeaw.ac.at/8435-5 (2018)
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