activity
20192022
most citedYou Impress Me: Dialogue Generation via Mutual Persona Perception

20 citations · 38 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CL20223 cited

Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems

Fan Zhou, Haoyu Dong, Qian Liu +3

Numerical reasoning over natural language has been a long-standing goal for the research community. However, cutting-edge language models have proven difficult to reliably generali…

cs.CL202212 cited

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…

cs.CL2021

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing

Qian Liu, Dejian Yang, Jiahui Zhang +3

Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language. To better understand and leverage what PLMs have…

cs.CL2021

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…

cs.CL2020

"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL

Yuntao Li, Bei Chen, Qian Liu +4

In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in t…

cs.CL20203 cited

Incomplete Utterance Rewriting as Semantic Segmentation

Qian Liu, Bei Chen, Jian-Guang Lou +2

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence…