UA lberta at S em E val-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment (2020)

Abstract: We investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our results demonstrate that the implementation of these ideas leads to improvements in predicting entailment.

## introduction in this paper, we discuss the university of alberta systems for semeval-2020 task 2: predicting multilingual and cross-lingual lexical entailment @xcite . we focus on the subtask of cross-lingual binary lexical entailment (le). @xcite define this task as "the task of detecting whether the meaning of a word in one language can be inferred from the meaning of a word in another language." they note its potential applications to machine translation, question answering, as well as to cross-lingual inference and entity linking. le is related to hypernym detection, with the former being more general @xcite . our principal objective is to provide evidence for the hypothesis that translations are useful in predicting cross-lingual entailment. it has been observed in prior work on cross-lingual lexical semantics that translations may be broader in meaning than the original text @xcite . in particular, translations may represent concepts entailed by the translated concept. for example, from the english phrase "you gave me the bottle", and its italian translation "mi hai dato il contenitore", it can be inferred that bottle entails contenitore ("container"). 1 we are interested in leveraging this phenomenon to perform unsupervised le prediction. our use of bitexts, word embeddings, and multilingual wordnets builds upon prior work. @xcite observe that similar words share entailments, and so semantic similarity can be used to detect additional entailment pairs. cross-lingual word embedding similarity has been used by @xcite to identify translations, and by @xcite to detect frequent word senses. @xcite leverage bitext alignment for textual entailment. the principal contribution of this paper is the presentation and evaluation of le prediction methods that leverage: (1) translation mining for entailment classification; (2) monolingual word embeddings for expanding the set of entailment pairs; and (3) multilingual lexical resources for improving translation alignment. to the best of our knowledge, we are the first to apply these ideas to cross-lingual le prediction, and demonstrate that each of them improves le prediction performance, especially in a low-resource setting. ## methods in this section, we outline our bitext-based approach to predicting cross-lingual entailment. section 2.1 describes our base method, bitext, which mines entailment pairs from word alignments in a bilingual parallel corpus. section 2.2 describes an enhanced method, vectors, which identifies additional entailment pairs based on estimates of semantic similarity. section 2.3 outlines our use of a knowledgebased alignment method, babalign, to further improve entailment coverage. ## experiments in this section, we describe the details of our experiments, including tools, setup, results, and implications. at the end, we discuss an additional experiment in which we use the hypernymy relation in babelnet as a proxy for lexical entailment. ## conclusion we have demonstrated a strong connection between translations and cross-lingual entailment. the sparsity of the translation data can be alleviated by the use of semantic similarity between word embeddings. finally, applying knowledge-based word alignment results in substantial improvements in identifying entailment in bitexts.