| Challenge: | Commonsense reasoning is a language-agnostic process, but most comprehensive knowledge sources are limited to a small number of languages, especially English. |
| Approach: | They propose to use English as a pivot language to integrate commonsense reasoning into models using a translate-retrieve-translate strategy. |
| Outcome: | The proposed model outperforms the state-of-the-art on the XCSR benchmarks. |
Similar Papers
mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans (2024.findings-acl)
Copied to clipboard
| Challenge: | Currently, multilingual datasets are created through translation, which cannot evaluate such language-specific aspects. |
| Approach: | They propose to curate a dataset for language-specific knowledge and commonsense . they propose to use multilingual commonsensiaq to leverage language models for a more efficient construction . |
| Outcome: | The proposed method reduces the creation cost by using multilingual LMs to create QAs . the proposed approach is based on the construction process of CSQA but with language models . |
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning (2021.acl-long)
Copied to clipboard
| Challenge: | Using multilingual language models, commonsense reasoning research has been limited to English. |
| Approach: | They propose a Mickey Probe task to evaluate commonsense across languages . they propose X-CSQA and XCODAH datasets to be translated to 14 languages based on the Mickey corpus . |
| Outcome: | The proposed method significantly improves sentence representations beyond English. |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
| Approach: | They propose a framework to transfer commonsense knowledge stored in a neural commonsensing model to a general-purpose pre-trained language model. |
| Outcome: | Empirical results show that the proposed framework improves the model’s performance on downstream tasks that require commonsense reasoning. |
It’s All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning (2021.findings-acl)
Copied to clipboard
| Challenge: | gilbert et al.: commonsense reasoning is a key problem in natural language processing but its capabilities are still unstudied. gilland eetal.: a new approach to commonsensible reasoning is needed to solve the problem. |
| Approach: | They propose a method which trains a linear classifier with weights of multi-head attention as features and a multilingual Winograd Schema corpus to measure cross-lingual generalization ability. |
| Outcome: | The proposed approach performs competitively with recent approaches even when applied to other languages in a zero-shot manner. |
Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion (C18-1)
Copied to clipboard
| Challenge: | Existing efforts to build commonsense knowledge bases are expensive and lack quantity and quality between languages. |
| Approach: | They propose to project English commonsense knowledge into Japanese and Chinese with high precision. |
| Outcome: | The proposed method achieves top-10 accuracy on the crowdsourced English–Japanese benchmark and 18,747 facts of accurate Japanese commonsense within a very short period. |
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning (2020.emnlp-main)
Copied to clipboard
| Challenge: | XCOPA dataset provides a typologically diverse dataset for commonsense reasoning in 11 languages . current methods for evaluating commonsensible reasoning in resource-poor languages are weak compared to translation-based transfer. |
| Approach: | They propose a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. |
| Outcome: | The proposed model performs better than current methods on a resource-poor dataset compared to translation-based transfer in the 11 languages studied . |
Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)
Copied to clipboard
| Challenge: | xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. |
| Approach: | They propose a multilingual structured reasoning and explanation dataset that covers four tasks across six languages and extends the English STREET benchmark to 5 additional diverse languages. |
| Outcome: | The proposed models show improved multilingual performance on scientific commonsense reasoning subtasks and no regression on non-reasoning tasks. |
CLICKER: Cross-Lingual Knowledge Editing via In-Context Learning with Adaptive Stepwise Reasoning (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing knowledge editing methods are static and fail to propagate edits across languages. |
| Approach: | They propose a KE method that dynamically retrieves only knowledge relevant to a given query and edits it to maintain cross-lingual consistency. |
| Outcome: | The proposed method outperforms static KE methods on a multilingual dataset with semantically similar but irrelevant prompts. |
KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)
Copied to clipboard
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency. |
| Approach: | They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task. |
| Outcome: | The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability. |