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我研究了3个例子：北京大学的wiki2bio、谷歌的ToTTo、微软的WIKITABLETEXT

北京大学的wiki2bio

• 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，不是二维表格

谷歌的ToTTo

Running with the following variables:
PREDICTION_PATH   : language/totto/sample/output_sample.txt
TARGET_PATH       : language/totto/sample/dev_sample.jsonl 
OUTPUT_DIR        : temp
MODE              : test
Writing references.
Writing tables in PARENT format.
Preparing predictions.
Writing predictions.
Running detokenizers.
======== EVALUATE OVERALL ========
Computing BLEU (overall)
BLEU+case.mixed+numrefs.3+smooth.exp+tok.13a+version.1.2.6 = 45.55 86.0/63.5/44.7/31.0 (BP = 0.869 ratio = 0.877 hyp_len = 57 ref_len = 65)
Computing PARENT (overall)
Evaluated 5 examples.
Precision = 76.11 Recall = 43.83 F-score = 53.34
======== EVALUATE OVERLAP SUBSET ========
Computing BLEU (overlap subset)
BLEU+case.mixed+numrefs.3+smooth.exp+tok.13a+version.1.2.6 = 37.15 84.8/56.7/37.0/25.0 (BP = 0.809 ratio = 0.825 hyp_len = 33 ref_len = 40)
Computing PARENT (overlap subset)
Evaluated 3 examples.
Precision = 71.40 Recall = 31.35 F-score = 41.34
======== EVALUATE NON-OVERLAP SUBSET ========
Computing BLEU (non-overlap subset)
BLEU+case.mixed+numrefs.3+smooth.exp+tok.13a+version.1.2.6 = 58.26 87.5/72.7/55.0/38.9 (BP = 0.959 ratio = 0.960 hyp_len = 24 ref_len = 25)
Computing PARENT (non-overlap subset)
Evaluated 2 examples.
Precision = 83.17 Recall = 62.56 F-score = 71.35

• 代码：https://github.com/google-research/language.git，示例数据可以运行。
• 测试脚本依赖的https://github.com/moses-smt/mosesdecoder.git不需要完全下载，准备好temp/mosesdecoder/scripts/tokenizer/detokenizer.perl即可
• 代码中sacrebleu的调用路径可直接使用sacrebleu.tokenize_13a
• 参考https://github.com/xuehuiping/language/tree/master/language/totto

微软的WIKITABLETEXT

• Bao, J., Tang, D., Duan, N., Yan, Z., Lv, Y., Zhou, M., & Zhao, T. (2018). Table-to-text: Describing table region with natural language. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 5020–5027. https://arxiv.org/pdf/1805.11234.pdf
• https://github.com/tangduyu/Table-Intelligence 没下载下来

关于表格的文本生成：Table-to-Text

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原文地址：https://www.cnblogs.com/xuehuiping/p/13576891.html