Antonio Uva


2018

pdf bib
Injecting Relational Structural Representation in Neural Networks for Question Similarity
Antonio Uva | Daniele Bonadiman | Alessandro Moschitti
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.

pdf bib
Supervised Clustering of Questions into Intents for Dialog System Applications
Iryna Haponchyk | Antonio Uva | Seunghak Yu | Olga Uryupina | Alessandro Moschitti
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.

2017

pdf bib
Effective shared representations with Multitask Learning for Community Question Answering
Daniele Bonadiman | Antonio Uva | Alessandro Moschitti
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features. Unfortunately, DNNs usually require a lot of training data, especially for highly semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20% of relative improvement over standard DNNs.

2016

pdf bib
LiMoSINe Pipeline: Multilingual UIMA-based NLP Platform
Olga Uryupina | Barbara Plank | Gianni Barlacchi | Francisco J. Valverde Albacete | Manos Tsagkias | Antonio Uva | Alessandro Moschitti
Proceedings of ACL-2016 System Demonstrations

pdf bib
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)

pdf bib
“Who was Pietro Badoglio?” Towards a QA system for Italian History
Stefano Menini | Rachele Sprugnoli | Antonio Uva
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents QUANDHO (QUestion ANswering Data for italian HistOry), an Italian question answering dataset created to cover a specific domain, i.e. the history of Italy in the first half of the XX century. The dataset includes questions manually classified and annotated with Lexical Answer Types, and a set of question-answer pairs. This resource, freely available for research purposes, has been used to retrain a domain independent question answering system so to improve its performances in the domain of interest. Ongoing experiments on the development of a question classifier and an automatic tagger of Lexical Answer Types are also presented.