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Found 10 papers, 5 papers with code
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets.
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
Breaking Down Multilingual Machine Translation
While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning.
Are you doing what I say? On modalities alignment in ALFRED
ALFRED is a recently proposed benchmark that requires a model to complete tasks in simulated house environments specified by instructions in natural language.
Improving Dialogue State Tracking by Joint Slot Modeling
Dialogue state tracking models play an important role in a task-oriented dialogue system.
A Comprehensive Assessment of Dialog Evaluation Metrics
In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets.
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization
Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets.
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling
Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios.
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension
Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding.
