Search Results for author: Bernardo Magnini

Found 35 papers, 4 papers with code

Addressing Slot-Value Changes in Task-oriented Dialogue Systems through Dialogue Domain Adaptation

no code implementations RANLP 2021 Tiziano Labruna, Bernardo Magnini

Recent task-oriented dialogue systems learn a model from annotated dialogues, and such dialogues are in turn collected and annotated so that they are consistent with certain domain knowledge.

Dialogue State Tracking Domain Adaptation +1

How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks

no code implementations EMNLP (insights) 2020 Samuel Louvan, Bernardo Magnini

Although several works have addressed the role of data selection to improve transfer learning for various NLP tasks, there is no consensus about its real benefits and, more generally, there is a lack of shared practices on how it can be best applied.

Multi-Task Learning

Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey

no code implementations COLING 2020 Samuel Louvan, Bernardo Magnini

In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems.

Intent Classification Natural Language Understanding +3

Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

no code implementations PACLIC 2020 Samuel Louvan, Bernardo Magnini

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available.

Data Augmentation General Classification +2

Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

1 code implementation21 Jan 2020 Vevake Balaraman, Bernardo Magnini

In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history.

Language Modelling Task-Oriented Dialogue Systems

A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values

no code implementations1 Nov 2019 Vevake Balaraman, Bernardo Magnini

This makes extending the candidate list for a slot without model retaining infeasible and also has limitations in modelling for low resource domains where training data for slot values are expensive.

Dialogue State Tracking

Scalable Neural Dialogue State Tracking

1 code implementation22 Oct 2019 Vevake Balaraman, Bernardo Magnini

A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn.

Dialogue State Tracking

FASTDial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents

1 code implementation ACL 2019 Serra Sinem Tekiroglu, Bernardo Magnini, Marco Guerini

We present a novel abstraction framework called FASTDial for designing task oriented dialogue agents, built on top of the OpenDial toolkit.

A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents

no code implementations WS 2018 Marco Guerini, Sara Falcone, Bernardo Magnini

In task-oriented conversational agents, more attention has been usually devoted to assessing task effectiveness, rather than to \textit{how} the task is achieved.

Toward zero-shot Entity Recognition in Task-oriented Conversational Agents

no code implementations WS 2018 Marco Guerini, Simone Magnolini, Vevake Balaraman, Bernardo Magnini

We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time.

Zero-Shot Learning

TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks

no code implementations COLING 2016 Bernardo Magnini, Anne-Lyse Minard, Mohammed R. H. Qwaider, Manuela Speranza

This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies.

Active Learning Domain Adaptation

Distributed Representations of Lexical Sets and Prototypes in Causal Alternation Verbs

no code implementations3 Oct 2016 Edoardo Maria Ponti, Elisabetta Jezek, Bernardo Magnini

Lexical sets contain the words filling an argument slot of a verb, and are in part determined by selectional preferences.

Acquiring Opposition Relations among Italian Verb Senses using Crowdsourcing

no code implementations LREC 2016 Anna Feltracco, Simone Magnolini, Elisabetta Jezek, Bernardo Magnini

We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology.

T-PAS; A resource of Typed Predicate Argument Structures for linguistic analysis and semantic processing

no code implementations LREC 2014 Elisabetta Jezek, Bernardo Magnini, Anna Feltracco, Alessia Bianchini, Octavian Popescu

The goal of this paper is to introduce T-PAS, a resource of typed predicate argument structures for Italian, acquired from corpora by manual clustering of distributional information about Italian verbs, to be used for linguistic analysis and semantic processing tasks.

META-SHARE: One year after

no code implementations LREC 2014 Stelios Piperidis, Harris Papageorgiou, Christian Spurk, Georg Rehm, Khalid Choukri, Olivier Hamon, Nicoletta Calzolari, Riccardo Del Gratta, Bernardo Magnini, Christian Girardi

This paper presents META-SHARE (www. meta-share. eu), an open language resource infrastructure, and its usage since its Europe-wide deployment in early 2013.

The KnowledgeStore: an Entity-Based Storage System

no code implementations LREC 2012 Roldano Cattoni, Francesco Corcoglioniti, Christian Girardi, Bernardo Magnini, Luciano Serafini, Roberto Zanoli

The system allows (i) to import background knowledge about entities, in form of annotated RDF triples; (ii) to associate resources to entities by automatically recognizing, coreferring and linking mentions of named entities; and (iii) to derive new entities based on knowledge extracted from mentions.

Entity Extraction using GAN Entity Linking

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