Multiparty dialogue question answering (QA) in machine reading comprehension (MRC) is a challenging task due to its complex information flow interactions and logical QA inference. Existing models typically handle such QA tasks by decoupling dialogue information at both speaker and utterance levels. However, few of them consider the logical inference relations in multiparty dialogue QA, leading to suboptimal QA performance. To address this issue, this paper proposes a memory network with logical inference (LIMN) for extractive QA in multiparty dialogues. LIMN introduces an inference module, which is pretrained by incorporating plain QA articles as external knowledge. It generates logical inference-aware representations from latent space for multiparty dialogues. To further model complex interactions among logical dialogue contexts, questions and key-utterance information, a key-utterance-based interaction method is proposed for leverage. Moreover, a multitask learning strategy is adopted for robust MRC. Extensive experiments were conducted on Molweni and FriendsQA benchmarks, which included 25k and 10k questions, respectively. Comparative results showed that LIMN achieves state-of-the-art results on both benchmarks, demonstrating the enhancement of logical QA inference in multiparty dialogue QA tasks.

This paper studies the problem of unsupervised time series representation learning, which aims to map unlabeled time series data into a low-dimensional latent space for various downstream tasks. Previous works usually combine a range of augmentation strategies with contrastive learning to generate discriminative representations. However, these augmentation strategies could alter the original semantics of time series data, which could degrade the performance of representation learning. To solve this problem, this paper incorporates the large language model (LLM) agent to guide unsupervised time series representation learning and proposes a novel framework named Multi-Agent Collaboration for Time-series Representation Learning (MERIT). The core of our MERIT is to utilize three LLM agents to collaboratively generate positive views for time series data. In particular, we first design a retrieval agent to automatically identify the relevant time series data from a coarse candidate set. Then, these selected sequences are further utilized to enhance an augmentation agent which automatically selects reliable augmentation strategies from an augmentation strategy library. We also design a review agent to evaluate the quality of generated views and stop the generation process. These three agents are designed to work in a loop for effective time series representation learning. Extensive experiments on multiple time series datasets demonstrate the effectiveness of our MERIT in comparison with state-of-the-art baselines.