Search Results for author:
Found 7 papers, 2 papers with code
Less is More: Attention Supervision with Counterfactuals for Text Classification
We aim to leverage human and machine intelligence together for attention supervision.
Structure-Augmented Keyphrase Generation
The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text.
Label-Efficient Training for Next Response Selection
This paper studies label augmentation for training dialogue response selection.
Debiasing Event Understanding for Visual Commonsense Tasks
We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction. We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.
Retrieval-Augmented Controllable Review Generation
We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes.
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering
This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences.
