Challenges in Data-to-Document Generation

Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task... (read more)

PDF Abstract EMNLP 2017 PDF EMNLP 2017 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Data-to-Text Generation RotoWire Encoder-decoder + conditional copy BLEU 14.19 # 3
Data-to-Text Generation RotoWire (Content Ordering) Encoder-decoder + conditional copy DLD 15.42% # 3
Data-to-Text Generation Rotowire (Content Selection) Encoder-decoder + conditional copy Precision 29.49% # 3
Recall 36.18% # 3
Data-to-Text Generation RotoWire (Relation Generation) Encoder-decoder + conditional copy count 23.72 # 2
Precision 74.80% # 3

Methods used in the Paper


METHOD TYPE
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