Salvatore Romeo


Label Semantic Aware Pre-training for Few-shot Text Classification
Aaron Mueller | Jason Krone | Salvatore Romeo | Saab Mansour | Elman Mansimov | Yi Zhang | Dan Roth
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction. However, use of label-semantics during pre-training has not been extensively explored. We therefore propose Label Semantic Aware Pre-training (LSAP) to improve the generalization and data efficiency of text classification systems. LSAP incorporates label semantics into pre-trained generative models (T5 in our case) by performing secondary pre-training on labeled sentences from a variety of domains. As domain-general pre-training requires large amounts of data, we develop a filtering and labeling pipeline to automatically create sentence-label pairs from unlabeled text. We perform experiments on intent (ATIS, Snips, TOPv2) and topic classification (AG News, Yahoo! Answers). LSAP obtains significant accuracy improvements over state-of-the-art models for few-shot text classification while maintaining performance comparable to state of the art in high-resource settings.


Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Sawsan Alqahtani | Garima Lalwani | Yi Zhang | Salvatore Romeo | Saab Mansour
Findings of the Association for Computational Linguistics: EMNLP 2021

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.


Tanbih: Get To Know What You Are Reading
Yifan Zhang | Giovanni Da San Martino | Alberto Barrón-Cedeño | Salvatore Romeo | Jisun An | Haewoon Kwak | Todor Staykovski | Israa Jaradat | Georgi Karadzhov | Ramy Baly | Kareem Darwish | James Glass | Preslav Nakov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what’s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.


Adversarial Domain Adaptation for Duplicate Question Detection
Darsh Shah | Tao Lei | Alessandro Moschitti | Salvatore Romeo | Preslav Nakov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines
Salvatore Romeo | Giovanni Da San Martino | Alberto Barrón-Cedeño | Alessandro Moschitti
Proceedings of ACL 2018, System Demonstrations

Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.


ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora
Alberto Barrón-Cedeño | Daniele Bonadiman | Giovanni Da San Martino | Shafiq Joty | Alessandro Moschitti | Fahad Al Obaidli | Salvatore Romeo | Kateryna Tymoshenko | Antonio Uva
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

Neural Attention for Learning to Rank Questions in Community Question Answering
Salvatore Romeo | Giovanni Da San Martino | Alberto Barrón-Cedeño | Alessandro Moschitti | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Mitra Mohtarami | James Glass
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.

Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking
Alberto Barrón-Cedeño | Giovanni Da San Martino | Salvatore Romeo | Alessandro Moschitti
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions. Unlike other social media, the length of the posted queries has no limits and queries tend to be multi-sentence elaborations combining context, actual questions, and irrelevant information. We approach the problem of question ranking: given a user’s new question, to retrieve those previously-posted questions which could be equivalent, or highly relevant. This could prevent the posting of nearly-duplicate questions and provide the user with instantaneous answers. For the first time in cQA, we address the selection of relevant text —both at sentence- and at constituent-level— for parse tree-based representations. Our supervised models for text selection boost the performance of a tree kernel-based machine learning model, allowing it to overtake the current state of the art on a recently released cQA evaluation framework.

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An Interactive System for Exploring Community Question Answering Forums
Enamul Hoque | Shafiq Joty | Lluís Màrquez | Alberto Barrón-Cedeño | Giovanni Da San Martino | Alessandro Moschitti | Preslav Nakov | Salvatore Romeo | Giuseppe Carenini
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums. The system integrates state-of-the-art technology for answer search with a Web-based user interface specifically tailored to support the cQA forum readers. The answer search module automatically finds relevant answers for a new question by exploring related questions and the comments within their threads. The graphical user interface presents the search results and supports the exploration of related information. The system is running live at


Semantic-Based Multilingual Document Clustering via Tensor Modeling
Salvatore Romeo | Andrea Tagarelli | Dino Ienco
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)