11 citations · 43 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
Xu Yang, Hanwang Zhang, Chongyang Gao +1
Humans tend to decompose a sentence into different parts like \textsc{sth do sth at someplace} and then fill each part with certain content. Inspired by this, we follow the \textit…
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
Yiren Jian, Chongyang Gao, Soroush Vosoughi
Semantic representation learning for sentences is an important and well-studied problem in NLP. The current trend for this task involves training a Transformer-based sentence encod…
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Yiren Jian, Chongyang Gao, Soroush Vosoughi
The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm. F…
Embedding Hallucination for Few-Shot Language Fine-tuning
Yiren Jian, Chongyang Gao, Soroush Vosoughi
Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language mo…
Non-Parallel Text Style Transfer with Self-Parallel Supervision
Ruibo Liu, Chongyang Gao, Chenyan Jia +2
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping…
Knowledge Infused Decoding
Ruibo Liu, Guoqing Zheng, Shashank Gupta +5
Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factuall…