We introduce AmbigNLG, a novel task designed to tackle the challenge of task ambiguity in instructions for Natural Language Generation (NLG).Ambiguous instructions often impede the performance of Large Language Models (LLMs), especially in complex NLG tasks.To tackle this issue, we propose an ambiguity taxonomy that categorizes different types of instruction ambiguities and refines initial instructions with clearer specifications.Accompanying this task, we present AmbigSNI NLG 1 , a dataset consisting of 2,500 annotated instances to facilitate research on AmbigNLG.Through comprehensive experiments with state-of-theart LLMs, we demonstrate that our method significantly enhances the alignment of generated text with user expectations, achieving up to a 15.02-point increase in ROUGE scores.Our findings highlight the importance of addressing task ambiguity to fully harness the capabilities of LLMs in NLG tasks.Furthermore, we confirm the effectiveness of our method in practical settings involving interactive ambiguity mitigation with users, underscoring the benefits of leveraging LLMs for interactive clarification.

Grammatical Error Correction (GEC) should focus not only on correction accuracy but also on the interpretability of the results for language learners.However, existing neuralbased GEC models mostly focus on improving accuracy, while their interpretability has not been explored.Example-based methods are promising for improving interpretability, which use similar retrieved examples to generate corrections.Furthermore, examples are beneficial in language learning, helping learners to understand the basis for grammatically incorrect/correct texts and improve their confidence in writing.Therefore, we hypothesized that incorporating an example-based method into GEC could improve interpretability and support language learners.In this study, we introduce an Example-Based GEC (EB-GEC) that presents examples to language learners as a basis for correction result.The examples consist of pairs of correct and incorrect sentences similar to a given input and its predicted correction.Experiments demonstrate that the examples presented by EB-GEC help language learners decide whether to accept or refuse suggestions from the GEC output.Furthermore, the experiments show that retrieved examples also improve the accuracy of corrections.

We address the task of antonym prediction in a context, which is a fill-in-the-blanks problem.This task setting is unique and practical because it requires contrastiveness to the other word and naturalness as a text in filling a blank.We propose methods for fine-tuning pre-trained masked language models (BERT) for contextaware antonym prediction.The experimental results show that these methods have positive impacts on the prediction of antonyms within a context.Moreover, human evaluation reveals that more than 85% of the predictions using the proposed method are acceptable as antonyms.

An advertising slogan is a sentence that expresses a product or a work of art in a straightforward manner and is used for advertising and publicity. Moving the consumer's mind and attracting their interest can significantly influence sales. Although rhetorical techniques in a slogan are known to improve the effectiveness of advertising, not much attention has been devoted to analyze or automatically generate sentences with the techniques. Therefore, we constructed a large corpus of slogans and revealed the linguistic characteristics of the basic statistics and rhetorical devices. Another point of focus was antitheses, of which the usage rates are relatively high and which have a specific sentence structure and lexical constraints. The generation of a slogan that contains an antithesis necessitates the structure of sentences, known as templates, to be extracted and also requires knowledge of word pairs with semantic contrast. Thus, the next step involved analysis of the structure to extract the sentence structure and lexical knowledge about the antithesis. Despite its simple architecture, the proposed method exceeds the prediction accuracy and efficiency of a comparable method. Lexical knowledge that is not available in existing dictionaries was also extracted.