而这个问题或许需要等到后续LLM能力更上一层楼时才能得到解决。 参考文献 1 HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation web3.arxiv.org/abs/2311 发布于 2024-06-02 10:22・广东 AI大模型 赞同7添加评论 分享喜欢收藏申请转载 ...
Local addressing determines which particular word in the table should be focused on while generating a piece of description at certain time step. However, the word level addressing can not fully address the table-to-text generation problem as the factual tables usually have complex structures which ...
Table-to-Text generationSeq2SeqHierarchical encoderIn this paper, we present a neural model to map structured table into document-scale descriptive texts. Most existing neural network based approaches encode a table record-by-record and generate long summaries by attentional encoder-decoder model, ...
Liu, T., Wang, K., Sha, L., Chang, B., & Sui, Z. (2018). Table-to-text generation by structure-aware seq2seq learning. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 4881–4888.https://arxiv.org/pdf/1711.09724.pdf https://github.com/tyliupku/wiki2bio 注意数据格式...
Therefore, in this paper, we introduce the novel task of Vision-augmented Table-To-Text Generation (VISTOT), defined as follows: given a table and an associated image, produce a descriptive sentence conditioned on the multimodal input. For the task, we present a novel multimodal t...
数据到文本的生成方法指的是从非文本的输入中生成描述性文本的任务。输入种类不同,任务可以定义地更加明确,比如摘要信息生成文本,信息框生成文本,图生成文本。 在这些任务中,我们关注逻辑表到文本的生成任务,这项任务旨在从表格生成流畅的但是逻辑正确的文本。而逻辑推理是一种高级的智能,这对现实中的文本生成系统时比...
De-Confounded Variational Encoder-Decoder for LogicalTable-to-Text Generation,原文地址介绍数据到文本的生成方法指的是从非文本的输入中生成描述性文本的任务。输入种类不同,任务可以定义地更加明确,比如摘要信息生成文本,信息框生成文本,图生成文本。在这些任务
🧞 TabGenie: A Toolkit for Table-to-Text Generation Demo 👉️ https://quest.ms.mff.cuni.cz/nlg/tabgenie TabGenie provides tools for working with data-to-text generation datasets in a unified tabular format. TabGenie allows you to: explore the content of the datasets interact with tabl...
Generating fluent, coherent, and informative text from structured data is called table-to-text generation. Copying words from the table is a common method to solve the “out-of-vocabulary” problem, but it’s difficult to achieve accurate copying. In order to overcome this problem, we invent ...
(i.e., PCG), for few-shot table-to-text generation. We prepend a task-specific prefix for a PLM to make the table structure better fit the pre-trained input. In addition, we generate an input-specific prefix to control the factual contents and word order of the generated text. Both ...