no code implementations • 5 May 2022 • Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty, Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr Panov, Kavya Srinet, Arthur Szlam, Yuxuan Sun, Marc-Alexandre Côté Katja Hofmann, Ahmed Awadallah, Linar Abdrazakov, Igor Churin, Putra Manggala, Kata Naszadi, Michiel van der Meer, Taewoon Kim
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
no code implementations • 13 Oct 2021 • Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty, Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr Panov, Kavya Srinet, Arthur Szlam, Yuxuan Sun, Katja Hofmann, Michel Galley, Ahmed Awadallah
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance.
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
We consider the additive decomposition problem in primitive towers and present an algorithm to decompose a function in an S-primitive tower as a sum of a derivative in the tower and a remainder which is minimal in some sense.
The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.
This paper extends the classical Ostrogradsky-Hermite reduction for rational functions to more general functions in primitive extensions of certain types.
Symbolic Computation 33F10, 68W30, 12H05
Based on this reduction algorithm, we design a new method to compute minimal telescopers for bivariate hyperexponential functions.
Symbolic Computation Combinatorics 33F10