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Found 16 papers, 7 papers with code
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision.
Ranked #1 on
Graph structure learning
on Cora
Diverse Keyphrase Generation with Neural Unlikelihood Training
Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well.
A Study of State Aliasing in Structured Prediction with RNNs
We show through extensive experiments and analysis that, when trained with policy gradient, recurrent neural networks often fail to learn a state representation that leads to an optimal policy in settings where the same action should be taken at different states.
From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems.
Towards Solving Text-based Games by Producing Adaptive Action Spaces
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Conditional text-to-image generation is an active area of research, with many possible applications.
Ranked #2 on
Text-to-Image Generation
on GeNeVA (i-CLEVR)
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting.
A Frame Tracking Model for Memory-Enhanced Dialogue Systems
Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems.
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
We developed this dataset to study the role of memory in goal-oriented dialogue systems.
A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems
The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions.
Policy Networks with Two-Stage Training for Dialogue Systems
Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL.
NASTIA: Negotiating Appointment Setting Interface
Appointment scheduling is a hybrid task halfway between slot-filling and negotiation.
DINASTI: Dialogues with a Negotiating Appointment Setting Interface
NASTIA is a reinforcement learning-based system.
