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.

Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods.