# A logical-based corpus for cross-lingual evaluation

## TL;DR

This paper introduces a new syntactic corpus for cross-lingual evaluation of logical inference, revealing strengths and limitations of models like BERT in handling complex linguistic structures across languages.

## Contribution

It proposes a novel set of syntactic tasks focused on logical forms for cross-lingual evaluation and demonstrates transfer learning between English and Portuguese.

## Key findings

- BERT outperforms recurrent models on most logical forms
- Counting operators remain challenging for BERT
- Cross-lingual transfer from English to Portuguese is successful

## Abstract

At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese.