27 citations · 113 across the 17 of their papers we have counts for
11 papers · 1 filter
Learning Dialogue Representations from Consecutive Utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao +4
Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data sc…
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Ribeiro, Shen Wang, Xiaofei Ma +8
Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations,…
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Zheng Li, Zijian Wang, Ming Tan +5
Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge…
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Andy T. Liu, Wei Xiao, Henghui Zhu +3
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 ef…
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
Xiaokai Wei, Shen Wang, Dejiao Zhang +2
Pretrained Language Models (PLM) have established a new paradigm through learning informative contextualized representations on large-scale text corpus. This new paradigm has revol…
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
Dejiao Zhang, Wei Xiao, Henghui Zhu +2
Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain specific knowledge. This challenge is magnified in natu…
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu +5
Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world,…
Pairwise Supervised Contrastive Learning of Sentence Representations
Dejiao Zhang, Shang-Wen Li, Wei Xiao +4
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese los…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The f…
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
Yifan Gao, Henghui Zhu, Patrick Ng +7
In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system ne…
Instance-based Transfer Learning for Multilingual Deep Retrieval
Andrew O. Arnold, William W. Cohen
We focus on the problem of search in the multilingual setting. Examining the problems of next-sentence prediction and inverse cloze, we show that at large scale, instance-based tra…