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Found 25 papers, 7 papers with code
Assessing the Quality of Human-Generated Summaries with Weakly Supervised Learning
This paper explores how to automatically measure the quality of human-generated summaries, based on a Norwegian corpus of real estate condition reports and their corresponding summaries.
Incremental Dialogue Management: Survey, Discussion, and Implications for HRI
Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models.
Enhancing Naturalness in LLM-Generated Utterances through Disfluency Insertion
We demonstrate through a user study that the insertion of disfluencies significantly increase the perceived spontaneity of the generated speech.
Truthful Text Sanitization Guided by Inference Attacks
The purpose of text sanitization is to rewrite those text spans in a document that may directly or indirectly identify an individual, to ensure they no longer disclose personal information.
A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction
After this conversion, the text representation of the dialogue state graph is included as part of the prompt of a large language model used to decode the agent response.
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis
The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information.
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring
Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases.
Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
Scripted dialogues such as movie and TV subtitles constitute a widespread source of training data for conversational NLP models.
Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis.
Who's in Charge? Roles and Responsibilities of Decision-Making Components in Conversational Robots
Software architectures for conversational robots typically consist of multiple modules, each designed for a particular processing task or functionality.
GraphWOZ: Dialogue Management with Conversational Knowledge Graphs
We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state.
Bootstrapping Text Anonymization Models with Distant Supervision
Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals.
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods.
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions
This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals.
skweak: Weak Supervision Made Easy for NLP
skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain.
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems.
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles.
Redefining Context Windows for Word Embedding Models: An Experimental Study
Distributional semantic models learn vector representations of words through the contexts they occur in.
OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles
We present a new major release of the OpenSubtitles collection of parallel corpora.
Model-based Bayesian Reinforcement Learning for Dialogue Management
Reinforcement learning methods are increasingly used to optimise dialogue policies from experience.
