Search Results for author:
Found 17 papers, 7 papers with code
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.
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.
Evaluating the Knowledge Dependency of Questions
They fail to evaluate the MCQ's ability to assess the student's knowledge of the corresponding target fact.
Towards Practical Plug-and-Play Diffusion Models
For that, the existing practice is to fine-tune the guidance models with labeled data corrupted with noises.
Towards Zero-Shot Functional Compositionality of Language Models
Large Pre-trained Language Models (PLM) have become the most desirable starting point in the field of NLP, as they have become remarkably good at solving many individual tasks.
Evaluation of Question Generation Needs More References
To this end, we propose to paraphrase the reference question for a more robust QG evaluation.
Cross Encoding as Augmentation: Towards Effective Educational Text Classification
To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification.
Addressing Negative Transfer in Diffusion Models
Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods.
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function
Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones.
Multi-Architecture Multi-Expert Diffusion Models
In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME).
Addressing Cold Start Problem for End-to-end Automatic Speech Scoring
However, this study highlights the significant decrease in the performance of speech scoring systems in new question contexts, thereby identifying this as a cold start problem in terms of items.
Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models
This report introduces \texttt{EEVE-Korean-v1. 0}, a Korean adaptation of large language models that exhibit remarkable capabilities across English and Korean text understanding.
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.
Label-Efficient Training for Next Response Selection
This paper studies label augmentation for training dialogue response selection.
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.
Less is More: Attention Supervision with Counterfactuals for Text Classification
We aim to leverage human and machine intelligence together for attention supervision.
