225 citations · 631 across the 14 of their papers we have counts for
8 papers · 1 filter
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
Controllable Text Generation with Focused Variation
Lei Shu, Alexandros Papangelis, Yi-Chia Wang +5
This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from t…
Joint Contextual Modeling for ASR Correction and Language Understanding
Yue Weng, Sai Sumanth Miryala, Chandra Khatri +8
The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU…
Exploration Based Language Learning for Text-Based Games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan +5
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated languag…
Modeling Multi-Action Policy for Task-Oriented Dialogues
Lei Shu, Hu Xu, Bing Liu +1
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only o…
Flexibly-Structured Model for Task-Oriented Dialogues
Lei Shu, Piero Molino, Mahdi Namazifar +4
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, wher…
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Piero Molino, Yang Wang, Jiawei Zhang
Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering in…