Alessandro Moschitti

Distributional Neural Networks for Automatic Resolution of Crossword Puzzles

2015

Aliaksei Severyn, Massimo Nicosia, Gianni Barlacchi, Alessandro Moschitti. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2015.

ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora

2016

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). 2016.

Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics

2017 · arXiv (Cornell University)

We propose to use question answering (QA) data from Web forums to train chatbots from scratch, i.e., without dialog training data. First, we extract pairs of question and answer sentences from the typically much longer …

Transfer Learning for Sequence Labeling Using Source Model and Target Data

2019 · arXiv (Cornell University)

In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label …

Adversarial Domain Adaptation for Duplicate Question Detection

2018 · arXiv (Cornell University)

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 …

Reranking for Efficient Transformer-based Answer Selection

2020

IR-based Question Answering (QA) systems typically use a sentence selector to extract the answer from retrieved documents. Recent studies have shown that powerful neural models based on the Transformer can provide an accurate solution to …

Injecting Relational Structural Representation in Neural Networks for\n Question Similarity

2018 · arXiv (Cornell University)

Effectively using full syntactic parsing information in Neural Networks (NNs)\nto solve relational tasks, e.g., question similarity, is still an open problem.\nIn this paper, we propose to inject structural representations in NNs by (i)\nlearning an SVM …

Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

2015

Learning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-document pairs, in question answering -- question-answer pairs. However, before …

Twitter Sentiment Analysis with Deep Convolutional Neural Networks

2015

This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial …

SemEval-2016 Task 3: Community Question Answering

2016

Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak, Abed Alhakim Freihat, Jim Glass, Bilal Randeree. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 2016.

SemEval-2017 Task 3: Community Question Answering

2017

Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin, Karin Verspoor. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 2017.

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

2020

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large …