Simone Filice


2021

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VoiSeR: A New Benchmark for Voice-Based Search Refinement
Simone Filice | Giuseppe Castellucci | Marcus Collins | Eugene Agichtein | Oleg Rokhlenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Voice assistants, e.g., Alexa or Google Assistant, have dramatically improved in recent years. Supporting voice-based search, exploration, and refinement are fundamental tasks for voice assistants, and remain an open challenge. For example, when using voice to search an online shopping site, a user often needs to refine their search by some aspect or facet. This common user intent is usually available through a “filter-by” interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets. To our knowledge, no benchmark dataset exists for training or validating such contextual search understanding models. To bridge this gap, we introduce the first large-scale dataset of voice-based search refinements, VoiSeR, consisting of about 10,000 search refinement utterances, collected using a novel crowdsourcing task. These utterances are intended to refine a previous search, with respect to a search facet or attribute (e.g., brand, color, review rating, etc.), and are manually annotated with the specific intent. This paper reports qualitative and empirical insights into the most common and challenging types of refinements that a voice-based conversational search system must support. As we show, VoiSeR can support research in conversational query understanding, contextual user intent prediction, and other conversational search topics to facilitate the development of conversational search systems.

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Learning to Solve NLP Tasks in an Incremental Number of Languages
Giuseppe Castellucci | Simone Filice | Danilo Croce | Roberto Basili
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of target languages grows. Moreover, the original annotated material may no longer be available due to storage or business constraints. Re-training only with the new language data will inevitably result in Catastrophic Forgetting of previously acquired knowledge. We propose a Continual Learning strategy that updates a model to support new languages over time, while maintaining consistent results on previously learned languages. We define a Teacher-Student framework where the existing model “teaches” to a student model its knowledge about the languages it supports, while the student is also trained on a new language. We report an experimental evaluation in several tasks including Sentence Classification, Relational Learning and Sequence Labeling.

2020

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Proceedings of the First International Workshop on Natural Language Processing Beyond Text
Giuseppe Castellucci | Simone Filice | Soujanya Poria | Erik Cambria | Lucia Specia
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

2017

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KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering
Simone Filice | Giovanni Da San Martino | Alessandro Moschitti
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team’s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.

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Deep Learning in Semantic Kernel Spaces
Danilo Croce | Simone Filice | Giuseppe Castellucci | Roberto Basili
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature representations during training. However, their input is tensor data, i.e., they can not manage rich structured information. In this paper, we show that expressive kernels and deep neural networks can be combined in a common framework in order to (i) explicitly model structured information and (ii) learn non-linear decision functions. We show that the input layer of a deep architecture can be pre-trained through the application of the Nystrom low-rank approximation of kernel spaces. The resulting “kernelized” neural network achieves state-of-the-art accuracy in three different tasks.

2016

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Learning to Recognize Ancillary Information for Automatic Paraphrase Identification
Simone Filice | Alessandro Moschitti
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Simone Filice | Danilo Croce | Alessandro Moschitti | Roberto Basili
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Global Thread-level Inference for Comment Classification in Community Question Answering
Shafiq Joty | Alberto Barrón-Cedeño | Giovanni Da San Martino | Simone Filice | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Structural Representations for Learning Relations between Pairs of Texts
Simone Filice | Giovanni Da San Martino | 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 1: Long Papers)

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Thread-Level Information for Comment Classification in Community Question Answering
Alberto Barrón-Cedeño | Simone Filice | Giovanni Da San Martino | Shafiq Joty | Lluís Màrquez | Preslav Nakov | 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)

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KeLP: a Kernel-based Learning Platform for Natural Language Processing
Simone Filice | Giuseppe Castellucci | Danilo Croce | Roberto Basili
Proceedings of ACL-IJCNLP 2015 System Demonstrations

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QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
Massimo Nicosia | Simone Filice | Alberto Barrón-Cedeño | Iman Saleh | Hamdy Mubarak | Wei Gao | Preslav Nakov | Giovanni Da San Martino | Alessandro Moschitti | Kareem Darwish | Lluís Màrquez | Shafiq Joty | Walid Magdy
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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UNITOR: Aspect Based Sentiment Analysis with Structured Learning
Giuseppe Castellucci | Simone Filice | Danilo Croce | Roberto Basili
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis
Giuseppe Castellucci | Simone Filice | Danilo Croce | Roberto Basili
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)