Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots (2021)

Abstract: Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards generating impressive fluent sentences by fine-tuning powerful pre-trained language models, the faithfulness of generated content still needs to be improved. To this end, this paper proposes a novel approach Attend, Memorize and Generate (called AMG), inspired by the text generation process of humans. In particular, AMG (1) attends over the multi-granularity of context using a novel strategy based on table slot level and traditional token-by-token level attention to exploit both the table structure and natural linguistic information; (2) dynamically memorizes the table slot allocation states; and (3) generates faithful sentences according to both the context and memory allocation states. Comprehensive experiments with human evaluation on three domains (i.e., humans, songs, and books) of the Wiki dataset show that our model can generate higher qualified texts when compared with several state-of-the-art baselines, in both fluency and faithfulness.

## introduction table-to-text generation, which aims to translate a semi-structured table into natural language descriptions while preserving the conveyed table information, are drawing increasing interest over the past few years. it has been widely applied in many real-world scenarios, such as automatically generating weather forecasting reports @xcite , biographies @xcite , restaurant descriptions @xcite , task-oriented conversations @xcite as well as healthcare descriptions @xcite hasan and 1 all the source code and experimental dataset are available at https://github.com/wentinghome/amg . ## experiment in this section, we explore the following experimental questions: (1) can the proposed model generate fluent sentences?; and (2) is the generated sentence faithful to the fact given by input table ? we also perform ablation analysis to investigate the two main components of amg, namely the slot attention and slot memory mechanism. ## conclusion in this paper, we have proposed a novel approach amg for faithful table-to-text generation in few shots. we first attend over the multi-granularity of context using a novel span level and traditional token-by-token level attention strategy to exploit both the table structural and natural linguistic information. then, we design a memory unit to memorize the table slot allocation states dynamically. extensive experiments on three domains of wiki dataset verify the effectiveness of our proposed model on generating fluent and faithful descriptions from tables.