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Found 11 papers, 2 papers with code
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
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.
Summarization with Graphical Elements
Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people.
Towards Interactive Language Modeling
As a first contribution we present a road map in which we detail the steps that need to be taken towards interactive language modeling.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
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.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.
What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research
Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.
Conditional Image Generation and Manipulation for User-Specified Content
This can be done by conditioning the model on additional information.
Conversations with Documents. An Exploration of Document-Centered Assistance
Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.
Understanding Multi-Head Attention in Abstractive Summarization
Finally, we find that relative positions heads seem integral to summarization performance and persistently remain after pruning.
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization.
Faithfully Explaining Rankings in a News Recommender System
There is an increasing demand for algorithms to explain their outcomes.
