Andrea Madotto

End-to-End Question Answering Models for Goal-Oriented Dialog Learning

2019

The task of Next Utterance Classification in dialog learning highly resembles that of Question Answering, but there has not been much attention to applying models across the two fields, especially not in more practical dialog …

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

2020 · arXiv (Cornell University)

In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, …

Plug-and-Play Conversational Models

2020

There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide …

Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems

2018

End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases. In this paper, we propose a novel yet simple end-toend differentiable model called memoryto-sequence (Mem2Seq) to address this issue. Mem2Seq is the …

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

2019 · arXiv (Cornell University)

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or …

Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning

2020

Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power …

Survey of Hallucination in Natural Language Generation

2022 · ACM Computing Surveys

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading …