12 citations · 17 across the 5 of their papers we have counts for
7 papers
When Language Model Meets Private Library
Daoguang Zan, Bei Chen, Zeqi Lin +3
With the rapid development of pre-training techniques, a number of language models have been pre-trained on large-scale code corpora and perform well in code generation. In this pa…
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
Shengnan An, Yifei Li, Zeqi Lin +6
Recently the prompt-tuning paradigm has attracted significant attention. By only tuning continuous prompts with a frozen pre-trained language model (PLM), prompt-tuning takes a ste…
Learning Algebraic Recombination for Compositional Generalization
Chenyao Liu, Shengnan An, Zeqi Lin +6
Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamica…
Iterative Utterance Segmentation for Neural Semantic Parsing
Yinuo Guo, Zeqi Lin, Jian-Guang Lou +1
Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To…
Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization
Yinuo Guo, Hualei Zhu, Zeqi Lin +3
Human intelligence exhibits compositional generalization (i.e., the capacity to understand and produce unseen combinations of seen components), but current neural seq2seq models la…
Hierarchical Poset Decoding for Compositional Generalization in Language
Yinuo Guo, Zeqi Lin, Jian-Guang Lou +1
We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset). Current encoder-decoder architectures do not take the…