Differing from sentiment transfer, positive reframing seeks to substitute negative perspectives with positive expressions while preserving the original meaning. With the emergence of pre-trained language models (PLMs), it is possible to achieve acceptable results by fine-tuning PLMs. Nevertheless, generating fluent, diverse and task-constrained reframing text remains a significant challenge. To tackle this issue, a **m**ulti-**s**trategy **o**ptimization **f**ramework (MSOF) is proposed in this paper. Starting from the objective of positive reframing, we first design positive sentiment reward and content preservation reward to encourage the model to transform the negative expressions of the original text while ensuring the integrity and consistency of the semantics. Then, different decoding optimization approaches are introduced to improve the quality of text generation. Finally, based on the modeling formula of positive reframing, we propose a multi-dimensional re-ranking method that further selects candidate sentences from three dimensions: strategy consistency, text similarity and fluency. Extensive experiments on two Seq2Seq PLMs, BART and T5, demonstrate our framework achieves significant improvements on unconstrained and controlled positive reframing tasks.

Emotion Recognition in Conversation (ERC) has attracted increasing attention due to its wide applications in public opinion analysis, empathetic conversation generation, and so on. However, ERC research suffers from the problems of data imbalance and the presence of similar linguistic expressions for different emotions. These issues can result in limited learning for minority emotions, biased predictions for common emotions, and the misclassification of different emotions with similar linguistic expressions. To alleviate these problems, we propose a Contrast-Enhanced Prompt-Tuning (CEPT) framework for ERC. We transform the ERC task into a Masked Language Modeling (MLM) generation task and generate the emotion for each utterance in the conversation based on the prompt-tuning of the Pre-trained Language Model (PLM), where a novel mixed prompt template and a label mapping strategy are introduced for better context and emotion feature modeling. Moreover, Supervised Contrastive Learning (SCL) is employed to help the PLM mine more information from the labels and learn a more discriminative representation space for utterances with different emotions. We conduct extensive experiments and the results demonstrate that CEPT outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions.

Commonsense question answering (QA) requires machines to utilize the QA content and external commonsense knowledge graph (KG) for reasoning when answering questions. Existing work uses two independent modules to model the QA contextual text representation and relationships between QA entities in KG, which prevents information sharing between modules for co-reasoning. In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer. The structure builds bridges to connect each layer of the text encoder and graph encoder, which can introduce the QA entity relationship from KG to the text encoder and bring contextual text information to the graph encoder, so that these features can be deeply interactively fused to form comprehensive text and graph node representations. Meanwhile, we propose a QA-aware node based KG subgraph construction method. The QA-aware nodes aggregate the question entity nodes and the answer entity nodes, and further guide the expansion and construction process of the subgraph to enhance the connectivity and reduce the introduction of noise. We evaluate our model on QA benchmarks in the CommonsenseQA and OpenBookQA datasets, and CORN achieves state-of-the-art performance.