Spoken-only languages are languages without a writing system. They remain excluded from modern Natural Language Processing (NLP) advancements like Large Language Models (LLMs) due to their lack of textual data. Existing NLP research focuses primarily on high-resource or written low-resource languages, leaving spoken-only languages critically underexplored. As a popular NLP paradigm, LLMs have demonstrated strong few-shot and cross-lingual generalization abilities, making them a promising solution for understanding and translating spoken-only languages. In this paper, we investigate how LLMs can translate spoken-only languages into high-resource languages by leveraging international phonetic transcription as an intermediate representation. We propose UNILANG, a unified language understanding framework that learns to translate spoken-only languages via in-context learning. Through automatic dictionary construction and knowledge retrieval, UNILANG equips LLMs with more fine-grained knowledge for improving word-level semantic alignment. To support this study, we introduce the SOLAN dataset, which consists of Bai (a spoken-only language) and its corresponding translations in a high-resource language. A series of experiments demonstrates the effectiveness of UNILANG in translating spoken-only languages, potentially contributing to the preservation of linguistic and cultural diversity. Our dataset and code will be publicly released.

Knowledge retrieval and response generation are fundamental to task-oriented dialogue systems. However, dialogue context frequently contains noisy or irrelevant information, leading to sub-optimal result in knowledge retrieval. One possible approach to retrieving knowledge is to manually annotate standard queries for each dialogue. Yet, this approach is hindered by the challenge of data scarcity, as human annotation is costly. To solve the challenge, we propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue. It generates high-quality queries for knowledge retrieval in task-oriented dialogue solely using low-resource annotated queries. To strengthen the performance correlation between response generation and knowledge retrieval, we propose a retrieval preservation mechanism by further selecting the most relevant knowledge from retrieved top-K records and explicitly incorporating these as prompts to guide a generator in response generation. Experiments on three standard benchmarks demonstrate that our model and mechanism outperform previous state-of-the-art by 3.26% on average with two widely used evaluation metrics.

Figurative language in media such as memes, art, or comics has gained dramatic interest recently. However, the challenge remains in accurately justifying and explaining whether an image caption complements or contradicts the image it accompanies. To tackle this problem, we design a modal-supplement framework MAPPER consisting of a describer and thinker. The describer based on a frozen large vision model is designed to describe an image in detail to capture entailed semantic information. The thinker based on a finetuned large multi-modal model is designed to utilize description, claim and image to make prediction and explanation. Experiment results on a publicly available benchmark dataset from FigLang2024 Task 2 show that our method ranks at top 1 in overall evaluation, the performance exceeds the second place by 28.57%. This indicates that MAPPER is highly effective in understanding, judging and explaining of the figurative language. The source code is available at https://github.com/Libv-Team/figlang2024.