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Found 37 papers, 4 papers with code
Addressing Slot-Value Changes in Task-oriented Dialogue Systems through Dialogue Domain Adaptation
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.
Using WordNet to Build Lexical Sets for Italian Verbs
We present a methodology for building lexical sets for argument slots of Italian verbs.
How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks
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.
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey
This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems.
Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain
While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly).
Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations
Large pre-trained language models have exhibited unprecedented capabilities in producing high-quality text via prompting techniques.
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey
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.
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification
Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available.
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language
We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian.
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.
Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems
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.
A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values
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.
Scalable Neural Dialogue State Tracking
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.
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Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?
Slot filling is a core operation for utterance understanding in task-oriented dialogue systems.
FASTDial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents
We present a novel abstraction framework called FASTDial for designing task oriented dialogue agents, built on top of the OpenDial toolkit.
Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system.
A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents
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
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.
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks
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.
Distributed Representations of Lexical Sets and Prototypes in Causal Alternation Verbs
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
We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology.
META-SHARE: One year after
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 Strategic Impact of META-NET on the Regional, National and International Level
This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics.
T-PAS; A resource of Typed Predicate Argument Structures for linguistic analysis and semantic processing
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.
A Corpus of Scientific Biomedical Texts Spanning over 168 Years Annotated for Uncertainty
Uncertainty language permeates biomedical research and is fundamental for the computer interpretation of unstructured text.
The KnowledgeStore: an Entity-Based Storage System
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.
