Cross-lingual Alignment of Knowledge Graph Triples with Sentences (2021)

Abstract: The pairing of natural language sentences with knowledge graph triples is essential for many downstream tasks like data-to-text generation, facts extraction from sentences (semantic parsing), knowledge graph completion, etc. Most existing methods solve these downstream tasks using neural-based end-to-end approaches that require a large amount of well-aligned training data, which is difficult and expensive to acquire. Recently various unsupervised techniques have been proposed to alleviate this alignment step by automatically pairing the structured data (knowledge graph triples) with textual data. However, these approaches are not well suited for low resource languages that provide two major challenges: (1) unavailability of pair of triples and native text with the same content distribution and (2) limited Natural language Processing (NLP) resources. In this paper, we address the unsupervised pairing of knowledge graph triples with sentences for low resource languages, selecting Hindi as the low resource language. We propose cross-lingual pairing of English triples with Hindi sentences to mitigate the unavailability of content overlap. We propose two novel approaches: NER-based filtering with Semantic Similarity and Key-phrase Extraction with Relevance Ranking. We use our best method to create a collection of 29224 well-aligned English triples and Hindi sentence pairs. Additionally, we have also curated 350 human-annotated golden test datasets for evaluation. We make the code and dataset publicly available.

## introduction the pairing of structural data (knowledge graphs, abstract meaning representations (amrs), tables, † https://www.dropbox. com/sh/lrh5q9odadixmqx/ aabrtt7yjn6-xvlvvinpqqm6a?@xmath0, etc.) with natural languages sentences has led to the development of many downstream tasks such as relation extraction @xcite , knowledge graph population @xcite , dialog generation @xcite , generation of natural text from structured data @xcite @xcite , etc. most existing methods solve above downstream tasks using neural-based end-to-end approaches that require a large amount of well-aligned humanannotated training data. however, the humanannotated dataset is expensive and difficult to obtain as annotators need to understand the structured data and natural text across various domains thoroughly. to overcome the lack of labeled data and difficulty in domain adaptation, unsupervised alignment has recently emerged as an active area of research @xcite @xcite . most of these unsupervised approaches utilize a large amount of structural and textual data having high content overlap. however, extending these approaches to low resource languages still poses a challenge due to the lack of structured data that has same content distribution as textual data. ## related work recently, there has been a lot of effort in creating automated structured data to text datasets in various domains. @xcite introduced a wikibio dataset by aligning opening sentences with infoboxes in english wikipedia articles on person's biographies. several extensions of this method of aligning wikipedia text with infoboxes have been proposed to create a dataset in different languages @xcite and domains @xcite . datasets created using these methods are constrained to a specific domain. @xcite alleviates this limitation by aligning knowledge graph triples in wikidata with opening sentences in wikipedia. it uses lexical overlap between the name entities present in a sentence, and wikidata triples for alignment. in addition to using triples available in wikidata (wikipedia's knowledge graph), @xcite introduced a dataset that also incorporates sub-property information in the form of quadruples. these datasets focus on aligning either knowledge graph triples or infoboxes with sentences present in wikipedia articles. @xcite introduced a dataset that combined the structured information residing in wikidata and infoboxes with a given sentence. to scale alignment of structured data with natural text across various domains @xcite introduced sequential pipeline strategy consisting of data collection, data filtering, entity linking, and alignment. additionally, it also suggests incorporating a human-annotated test dataset to evaluate the different alignment methods. all of the previous approaches depend upon lexical overlap between structured and textual data. these approaches are ineffective for cross-lingual alignment where structured data and textual data are in different languages. although, we can utilize previously proposed strategies for dataset creation by translating either structured data or textual data to other languages. webnlg 2020 (castro ferreira et al., 2020) shared task presents one such cross-lingual aligned dataset where shimorina et al. (2019) performs automatic translation and post editing of english sentences to russian. final dataset consists of english triples aligned with russain sentences verbalizing those triples. such approaches do incur the loss due to automatic translation though. later, we demonstrate that our proposed approach for cross-lingual alignment achieves comparatively better results. ## unsupervised cross-lingual alignment our alignment model aims to align the most relevant english triples to hindi sentences. we introduce two novel approaches for cross-lingual sentence and facts alignment task: 1) ner-based filtering with semantic similarity and 2) key-phrase extraction with relevance ranking. ner-based filtering with semantic similarity incorporates a novel idea for named entity disambiguation. we used nearest neighbor-based search to find the most relevant english words for the given hindi words in the sentence by projecting hindi and english words in the same vector space. we use multilingual unsupervised and supervised embeddings (muse) @xcite to obtain multilingual vector representation and then perform the nearest neighbor search to obtain the top-k candidates. the chosen candidates are further filtered based on semantic similarity, which boosts the precision of the model. we experiment with several state-of-the-art multilingual transformer-based models to find semantic similarities between facts and sentences. in key-phrase extraction with relevance ranking, we extract key phrases from a hindi sentence based on simple pos-tag-based heuristics and then rank extracted key phrases in the sentence to their relevance with its corresponding constituent article. we propose a new multilingual variant of em-bedrank @xcite to obtain rankings. top-k relevant triples are then selected based on similarity scores with the key phrases of a sentence. ## error analysis key-phrase extraction with relevance ranking: as per the ranking mechanism, we keep only the most relevant top ranked triples. however, we notice that in some cases, especially where there are multiple triples which convey similar information, the model misses to capture all the relevant triples. only the highest rank triples are considered, which leads to similar triples being missed out due to a slightly lower rank. in figure 4 , the first example is a predicted sample by the key-phrase extraction model. we observe that occupation:author and oc-