Partially-Aligned Data-to-Text Generation with Distant Supervision (2020)

Abstract: The Data-to-Text task aims to generate human-readable text for describing some given structured data enabling more interpretability. However, the typical generation task is confined to a few particular domains since it requires well-aligned data which is difficult and expensive to obtain. Using partially-aligned data is an alternative way of solving the dataset scarcity problem. This kind of data is much easier to obtain since it can be produced automatically. However, using this kind of data induces the over-generation problem posing difficulties for existing models, which tends to add unrelated excerpts during the generation procedure. In order to effectively utilize automatically annotated partially-aligned datasets, we extend the traditional generation task to a refined task called Partially-Aligned Data-to-Text Generation (PADTG) which is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. To tackle this new task, we propose a novel distant supervision generation framework. It firstly estimates the input data’s supportiveness for each target word with an estimator and then applies a supportiveness adaptor and a rebalanced beam search to harness the over-generation problem in the training and generation phases respectively. We also contribute a partially-aligned dataset (The data and source code of this paper can be obtained from https://github.com/fuzihaofzh/distant_supervision_nlg ) by sampling sentences from Wikipedia and automatically extracting corresponding KB triples for each sentence from Wikidata. The experimental results show that our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.

## wita: our partially-aligned dataset we automatically harvest some partially-aligned data from wikipedia and wikidata and prepare a dataset called wita. we select each wikipedia article's first sentence from the 20190920 wikipedia dump foot_1 as the target text. then, we remove irrelevant tags and links with several predefined rules. we propose a kb extractor, as illustrated in fig. 3 , which can take the selected wikipedia sentences and extract the corresponding kb triples. in the kb extractor, named entities are detected by an entity detector. the detected named entities are then combined into pairs by the cartesian product operation. the triples that mention these entity pairs are retrieved by a triple retriever that searches the corresponding kb triples from the wikidata company of heroes is a 2006 real-time strategy video game developed by relic entertainment depicting the second world war. company of heros, real-time strategy video game 2006, relic entertainment, the second world war company, heroes, relic entertainment, video game, the second world war company of heros, real-time strategy video game, relic entertainment, the second world war, 2006 company of heros relic entertainment 2006 strategy video game developer publication_date genre ner detector noun detector linking detector triple retriever database. we use an entity-recall based score to filter inappropriate sentences. ## related works during the past few years, many tasks have been proposed to generate human-readable text from the structured data. webnlg @xcite is proposed to describe kb triples sampled from dbpedia @xcite . the e2e @xcite task is proposed for generating restaurant reviews based on the given attributes. @xcite propose the wikibio task to generate people's biography based on given wikipedia infobox. @xcite propose to generate text based on event chains. moreover, @xcite propose to generate weather reports for weather records and @xcite , @xcite and @xcite propose to generate a match report according to the match briefing. all these datasets are restricted to a few domains where well-aligned data is happened to be available. no existing works are focusing on handling partiallyaligned data. to solve the dataset scarcity problem, @xcite propose to use dual learning to train generation models based on unaligned text and knowledge triples. the model generates text based on input triples and then predict the input triples with a dual extraction model. the two models are trained alternatively with dual learning. although @xcite proposed the ent-desc task aiming at generating better text description for a few entities by exploring the knowledge from kb, their focus is more on distilling the useful part from the input knowledge. text aligning has been studied for many years. @xcite propose the fast align model which is a log-linear reparameterization of ibm model 2. @xcite propose a new score aggregation method to improve the alignment result. moreover, attention-based models @xcite can also be recognized as a kind of alignment. however, these models focus on aligning source words to target words, and no existing models have been proposed to directly calculate supportiveness for generation tasks. in generation systems, @xcite propose to dynamically align the current generation state with topics to improve the generation performance. however, it still can not directly align to the input source words. ## conclusions in this work, we propose a new task, namely, partially-aligned data-to-text generation, in which we generate human-readable text based on automatically produced training data. this task is more practical and extensible to any domains. we propose a distant supervision generation framework that tackling the task. the experimental results show that our proposed model solves the over-generation problem effectively and outperforms all baseline models. moreover, we contribute a partially-aligned dataset wita produced by our novel automatically annotating framework which is suitable for this new task.