Search Results for author:
Found 8 papers, 5 papers with code
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
Ranked #1 on
Speaker Identification
on EVI fr-FR
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue
2) The ontology is divided into domain-specific and generic (i. e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples.
Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers
In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary.
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Identifying public misinformation is a complicated and challenging task.
Ranked #5 on
Fake News Detection
on FNC-1
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations.
Clinical Text Prediction with Numerically Grounded Conditional Language Models
In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion.
Numerically Grounded Language Models for Semantic Error Correction
Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction.
A Persona-Based Neural Conversation Model
We present persona-based models for handling the issue of speaker consistency in neural response generation.
