Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models

• URL: http://arxiv.org/abs/2306.10727v1
• Date: Mon, 19 Jun 2023 07:00:14 GMT
• Title: Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models
• Authors: Tomoki Sugimoto, Yasumasa Onoe, Hitomi Yanaka
• Abstract summary: We present Jamp, a Japanese benchmark focused on temporal inference. Our dataset includes a range of temporal inference patterns, which enables us to conduct fine-grained analysis. We evaluate the generalization capacities of monolingual/multilingual LMs by splitting our dataset based on tense fragments.
• Score: 18.874880342410876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural Language Inference (NLI) tasks involving temporal inference remain challenging for pre-trained language models (LMs). Although various datasets have been created for this task, they primarily focus on English and do not address the need for resources in other languages. It is unclear whether current LMs realize the generalization capacity for temporal inference across languages. In this paper, we present Jamp, a Japanese NLI benchmark focused on temporal inference. Our dataset includes a range of temporal inference patterns, which enables us to conduct fine-grained analysis. To begin the data annotation process, we create diverse inference templates based on the formal semantics test suites. We then automatically generate diverse NLI examples by using the Japanese case frame dictionary and well-designed templates while controlling the distribution of inference patterns and gold labels. We evaluate the generalization capacities of monolingual/multilingual LMs by splitting our dataset based on tense fragments (i.e., temporal inference patterns). Our findings demonstrate that LMs struggle with specific linguistic phenomena, such as habituality, indicating that there is potential for the development of more effective NLI models across languages.

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