Search Results for author:
Found 10 papers, 4 papers with code
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
Debiasing Neural Retrieval via In-batch Balancing Regularization
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16. 5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets.
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks.
Supporting Clustering with Contrastive Learning
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on
Short Text Clustering
on AG News
Uncertainty-Based Adaptive Learning for Reading Comprehension
Recent years have witnessed a surge of successful applications of machine reading comprehension.
Neural document expansion for ad-hoc information retrieval
Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task.
