Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops. Our code is available at https://github.com/luka-group/Lattice.
翻译:受控制的表格- 文本生成试图为表格中突出显示的子部分生成自然语言描述。 以前的 SOTA 系统仍然使用序列到序列生成方法, 仅将表格作为线性结构, 当表格布局改变时会变得不易。 我们试图超越这一范式, 方法是:(1) 有效地表达表格中内容片段的关系, (2) 使我们的模型对内容变化性结构转换具有强力性。 因此, 我们提议了一个等式学习框架, 将表格编码为结构上自觉的自留机制。 这把完整的自留结构输入成一个顺序- 变化图示关注器, 以捕捉到属于同一行或列的单元格的相连接的图形结构结构, 并在结构角度上区分相关的单元格和不相干细胞。 我们的框架还修改了定位编码机制, 以保持同一单元格中标物的相对位置, 并强制在不同单元格中执行不均匀的位置。 我们的技术可以被插入现有的表- 文本生成模型, 并且已经改进了基于 T5 的模型, 以显示属于同一行或列的单元格的单元格的图表结构结构结构结构,, 保存了我们有更强的性性 。