Search Results for author: Alessandro Moschitti

Found 51 papers, 12 papers with code

SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References

no code implementations21 Sep 2023 Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski, Alessandro Moschitti

Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions.

Answer Selection Sentence

Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages

no code implementations25 May 2023 Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti

While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets.

Knowledge Distillation Machine Translation +2

Context-Aware Transformer Pre-Training for Answer Sentence Selection

no code implementations24 May 2023 Luca Di Liello, Siddhant Garg, Alessandro Moschitti

Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline.

Ranked #4 on Question Answering on TrecQA (using extra training data)

Question Answering Sentence

Learning Answer Generation using Supervision from Automatic Question Answering Evaluators

no code implementations24 May 2023 Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti

Recent studies show that sentence-level extractive QA, i. e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style).

Answer Generation Question Answering +2

QUADRo: Dataset and Models for QUestion-Answer Database Retrieval

no code implementations30 Mar 2023 Stefano Campese, Ivano Lauriola, Alessandro Moschitti

For this purpose, we (i) build a large scale DB of 6. 3M q/a pairs, using public questions, (ii) design a new system based on neural IR and a q/a pair reranker, and (iii) construct training and test data to perform comparative experiments with our models.

Question Answering Retrieval

Effective Pre-Training Objectives for Transformer-based Autoencoders

no code implementations24 Oct 2022 Luca Di Liello, Matteo Gabburo, Alessandro Moschitti

In this paper, we study trade-offs between efficiency, cost and accuracy when pre-training Transformer encoders with different pre-training objectives.

Knowledge Transfer from Answer Ranking to Answer Generation

no code implementations23 Oct 2022 Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue.

Answer Generation Question Answering +2

Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection

no code implementations20 May 2022 Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents.

Answer Selection Sentence

Paragraph-based Transformer Pre-training for Multi-Sentence Inference

1 code implementation NAACL 2022 Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

Our evaluation on three AS2 and one fact verification datasets demonstrates the superiority of our pre-training technique over the traditional ones for transformers used as joint models for multi-candidate inference tasks, as well as when used as cross-encoders for sentence-pair formulations of these tasks.

Answer Selection Fact Verification +1

DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection

no code implementations NeurIPS Workshop DBAI 2021 Nic Jedema, Thuy Vu, Manish Gupta, Alessandro Moschitti

While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks.

Entity Linking Fact Verification +4

Question-Answer Sentence Graph for Joint Modeling Answer Selection

no code implementations16 Feb 2022 Roshni G. Iyer, Thuy Vu, Alessandro Moschitti, Yizhou Sun

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems.

Answer Selection Retrieval +1

In Situ Answer Sentence Selection at Web-scale

no code implementations16 Jan 2022 Zeyu Zhang, Thuy Vu, Alessandro Moschitti

Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i. e., sentences, extracted from the retrieved text.

Multi-Task Learning Open-Domain Question Answering +1

Double Retrieval and Ranking for Accurate Question Answering

no code implementations16 Jan 2022 Zeyu Zhang, Thuy Vu, Alessandro Moschitti

Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.

Answer Selection Retrieval

Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems

1 code implementation15 Jan 2022 Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti

CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.

Efficient Neural Network Question Answering +1

Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation

no code implementations14 Oct 2021 Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti

Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.

Answer Generation Generative Question Answering +3

Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering

no code implementations EMNLP 2021 Siddhant Garg, Alessandro Moschitti

In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them.

Question Answering

Joint Models for Answer Verification in Question Answering Systems

no code implementations ACL 2021 Zeyu Zhang, Thuy Vu, Alessandro Moschitti

This paper studies joint models for selecting correct answer sentences among the top $k$ provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems.

Question Answering Retrieval +1

Answer Generation for Retrieval-based Question Answering Systems

no code implementations Findings (ACL) 2021 Chao-Chun Hsu, Eric Lind, Luca Soldaini, Alessandro Moschitti

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.

Answer Generation Question Answering +2

AVA: an Automatic eValuation Approach for Question Answering Systems

no code implementations NAACL 2021 Thuy Vu, Alessandro Moschitti

We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy.

Question Answering Test

Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents

1 code implementation NAACL 2021 Iryna Haponchyk, Alessandro Moschitti

Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e. g., structured SVM or perceptron.

Clustering Representation Learning +1

Efficient pre-training objectives for Transformers

no code implementations20 Apr 2021 Luca Di Liello, Matteo Gabburo, Alessandro Moschitti

The Transformer architecture deeply changed the natural language processing, outperforming all previous state-of-the-art models.

Machine Translation Customization via Automatic Training Data Selection from the Web

1 code implementation20 Feb 2021 Thuy Vu, Alessandro Moschitti

Machine translation (MT) systems, especially when designed for an industrial setting, are trained with general parallel data derived from the Web.

Machine Translation Translation

Multilingual Answer Sentence Reranking via Automatically Translated Data

no code implementations20 Feb 2021 Thuy Vu, Alessandro Moschitti

We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems.

Question Answering Sentence

CDA: a Cost Efficient Content-based Multilingual Web Document Aligner

no code implementations EACL 2021 Thuy Vu, Alessandro Moschitti

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level.

Machine Translation Translation

Context-based Transformer Models for Answer Sentence Selection

1 code implementation1 Jun 2020 Ivano Lauriola, Alessandro Moschitti

An important task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question.

Question Answering Sentence

AVA: an Automatic eValuation Approach to Question Answering Systems

no code implementations2 May 2020 Thuy Vu, Alessandro Moschitti

This allows for effectively measuring the similarity between the reference and an automatic answer, biased towards the question semantics.

Question Answering Test

A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection

no code implementations COLING 2020 Daniele Bonadiman, Alessandro Moschitti

An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i. e., Answer Sentence Selection (A2S).

Question Answering Sentence

SemEval-2015 Task 3: Answer Selection in Community Question Answering

no code implementations SEMEVAL 2015 Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, James Glass, Bilal Randeree

Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e. g., the exploitation of the interaction between users and the structure of related posts.

Answer Selection Community Question Answering

Global Thread-Level Inference for Comment Classification in Community Question Answering

no code implementations EMNLP 2015 Shafiq Joty, Alberto Barrón-Cedeño, Giovanni Da San Martino, Simone Filice, Lluís Màrquez, Alessandro Moschitti, Preslav Nakov

Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd.

Community Question Answering General Classification

Transfer Learning for Sequence Labeling Using Source Model and Target Data

no code implementations14 Feb 2019 Lingzhen Chen, Alessandro Moschitti

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 categories appear.

named-entity-recognition Named Entity Recognition +2

Adversarial Domain Adaptation for Duplicate Question Detection

1 code implementation EMNLP 2018 Darsh J Shah, Tao Lei, Alessandro Moschitti, Salvatore Romeo, Preslav Nakov

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.

Domain Adaptation Question Similarity

Injecting Relational Structural Representation in Neural Networks for Question Similarity

1 code implementation ACL 2018 Antonio Uva, Daniele Bonadiman, Alessandro Moschitti

Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e. g., question similarity, is still an open problem.

Question Similarity

Integrating Stance Detection and Fact Checking in a Unified Corpus

no code implementations NAACL 2018 Ramy Baly, Mitra Mohtarami, James Glass, Lluis Marquez, Alessandro Moschitti, Preslav Nakov

A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.

Fact Checking Retrieval +1

Automatic Stance Detection Using End-to-End Memory Networks

no code implementations NAACL 2018 Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti

We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.

Stance Detection

Cross-Language Question Re-Ranking

no code implementations4 Oct 2017 Giovanni Da San Martino, Salvatore Romeo, Alberto Barron-Cedeno, Shafiq Joty, Lluis Marquez, Alessandro Moschitti, Preslav Nakov

We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings.

Machine Translation Re-Ranking +1

Multitask Learning with Deep Neural Networks for Community Question Answering

no code implementations13 Feb 2017 Daniele Bonadiman, Antonio Uva, Alessandro Moschitti

In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i. e., question-comment similarity, question-question similarity and new question-comment similarity.

Community Question Answering Feature Engineering +2

Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks

no code implementations5 Apr 2016 Aliaksei Severyn, Alessandro Moschitti

In this paper, we propose convolutional neural networks for learning an optimal representation of question and answer sentences.

Sentence Test

Semi-supervised Question Retrieval with Gated Convolutions

1 code implementation NAACL 2016 Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Katerina Tymoshenko, Alessandro Moschitti, Lluis Marquez

Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions.

Question Answering Retrieval

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