MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control

Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models. However, textual personas are limited in describing multi-faceted attributes (\emph{e.g.}, \emph{language style, inner character nuances}), implicit embedding suffers from personality sparsity, and handicraft prompts lack fine-grained and stable controllability. Hence, these approaches may struggle with complex personalized dialogue generation tasks that require generating controllable responses with multiple personal attributes. To this end, we propose \textbf{\textsc{Miracle}}, a novel personalized dialogue generation method through \textbf{M}ult\textbf{I}ple Pe\textbf{R}sonal \textbf{A}ttributes \textbf{C}ontrol within \textbf{L}atent-Space \textbf{E}nergy-based Models. ttributes \textbf{C}ontrol within \textbf{L}atent-Space \textbf{E}nergy-based Models. Specifically, our approach first disentangles complex personality into multi-faceted attributes. Subsequently, we employ a conditional variational auto-encoder to align with the dense personalized responses within a latent joint attribute space. We have also tailored a dedicated energy function and customized the ordinary differential equations sampling method to offer flexible attribute composition and precise attribute control. Extensive experiments demonstrate that \textsc{Miracle} outperforms several strong baselines in terms of personality controllability and response generation quality. Our dataset and code are available at \url{https://github.com/LZY-the-boys/MIRACLE}Comment: Accepted by EMNLP2023 finding

Target Guided Emotion Aware Chat Machine

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.Comment: To appear on TOIS 202

Simvastatin-Loaded Nanomicelles Enhance the Osteogenic Effect of Simvastatin

Objectives. The present study intended to further verify that simvastatin-loaded nanomicelles (SVNs) enhanced the role of simvastatin (SV) in promoting osteoblast differentiation in vitro and to evaluate the effect of SVNs on bone defect repair in vivo. Methods. SVNs were synthesized by dialysis. MG63 cells were subjected to intervention with 0.25 μmol/l of SVNs and SV. A 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay kit and flow cytometry were used to determine cell proliferation activity, cell cycle distribution, and apoptosis. The osteoblastic differentiation of MG 63 cells was evaluated by measuring alkaline phosphatase (ALP) activity, ALP staining, and the expression levels of the osterix (Osx) and osteocalcin (OC) proteins. In addition, 0.5 mg of SVNs or SV was applied to the skull defect area of rabbits. Micro-CT, hematoxylin and eosin (HE) staining, and Masson’s trichrome staining were used for qualitative and quantitative evaluation of new bone in three dimensions and two dimensions. Results. The SVNs had a mean diameter of 38.97 nm. The encapsulation and drug-loading efficiencies were 54.57±3.15% and 10.91±0.63%, respectively. In vitro, SVNs and SV can inhibit the proliferation activity and promote osteogenic differentiation of MG63 cells by arresting MG63 cells at the G0/G1 phase without increasing the apoptosis rate. In vivo quantitative results showed that the bone mineral density (BMD), bone volume (BV)/total volume (TV) ratio, and trabecular number (Tb.N) in the gelatin sponge with SVNs (SVNs-GS) group and gelatin sponge with SV (SV-GS) group were 362.1%, 292.0%; 181.3%, 158.0%; and 215.2%, 181.8% of those in the blank control (BC) group, respectively. Histological results identified the new bone tissue in each group as irregular fibrous bone, and the arrangement of trabecular bone was disordered. There were significantly more osteoblasts and new capillaries around the trabecular bone in the SVNs-GS group and SV-GS group than in both the BC and drug-free nanomicelle (DFNs) groups. Both in vitro and in vivo, SVNs exhibited greater osteogenic efficacy than SV. Conclusion. SVNs significantly improved the osteogenic efficacy of SV

OneRel: Joint Entity and Relation Extraction with One Module in One Step

Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or processing steps to make it easy to conduct. However, such a paradigm ignores the fact that the three elements of a triple are interdependent and indivisible. Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. Specifically, our model consists of a scoring-based classifier and a relation-specific horns tagging strategy. The former evaluates whether a token pair and a relation belong to a factual triple. The latter ensures a simple but effective decoding process. Extensive experimental results on two widely used datasets demonstrate that the proposed method performs better than the state-of-the-art baselines, and delivers consistent performance gain on complex scenarios of various overlapping patterns and multiple triples

Stack-VS : stacked visual-semantic attention for image caption generation

Recently, automatic image caption generation has been an important focus of the work on multimodal translation task. Existing approaches can be roughly categorized into two classes, top-down and bottom-up, the former transfers the image information (called as visual-level feature) directly into a caption, and the later uses the extracted words (called as semantic-level attribute) to generate a description. However, previous methods either are typically based one-stage decoder or partially utilize part of visual-level or semantic-level information for image caption generation. In this paper, we address the problem and propose an innovative multi-stage architecture (called as Stack-VS) for rich fine-grained image caption generation, via combining bottom-up and top-down attention models to effectively handle both visual-level and semantic-level information of an input image. Specifically, we also propose a novel well-designed stack decoder model, which is constituted by a sequence of decoder cells, each of which contains two LSTM-layers work interactively to re-optimize attention weights on both visual-level feature vectors and semantic-level attribute embeddings for generating a fine-grained image caption. Extensive experiments on the popular benchmark dataset MSCOCO show the significant improvements on different evaluation metrics, i.e., the improvements on BLEU-4 / CIDEr / SPICE scores are 0.372, 1.226 and 0.216, respectively, as compared to the state-of-the-art.Published versio