Table-to-text Generation by Structure-aware Seq2seq Learning

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.Comment: Accepted by AAAI201

Text Attribute Control via Closed-Loop Disentanglement

Changing an attribute of a text without changing the content usually requires to first disentangle the text into irrelevant attributes and content representations. After that, in the inference phase, the representation of one attribute is tuned to a different value, expecting that the corresponding attribute of the text can also be changed accordingly. The usual way of disentanglement is to add some constraints on the latent space of an encoder-decoder architecture, including adversarial-based constraints and mutual-information-based constraints. However, the previous semi-supervised processes of attribute change are usually not enough to guarantee the success of attribute change and content preservation. In this paper, we propose a novel approach to achieve a robust control of attributes while enhancing content preservation. In this approach, we use a semi-supervised contrastive learning method to encourage the disentanglement of attributes in latent spaces. Differently from previous works, we re-disentangle the reconstructed sentence and compare the re-disentangled latent space with the original latent space, which makes a closed-loop disentanglement process. This also helps content preservation. In addition, the contrastive learning method is also able to replace the role of minimizing mutual information and adversarial training in the disentanglement process, which alleviates the computation cost. We conducted experiments on three text datasets, including the Yelp Service review dataset, the Amazon Product review dataset, and the GoEmotions dataset. The experimental results show the effectiveness of our model.Comment: accepted by TACL 202

Harnessing the Plug-and-Play Controller by Prompting

Controllable text generation is a growing field within natural language generation (NLG) that focuses on producing text that meets specific constraints in real-world applications. Previous approaches, such as plug-and-play controllers (PPCs), aimed to steer the properties of generated text in a flexible manner. However, these methods often compromised the integrity of the language model's decoding process, resulting in less smooth text generation. Alternatively, other techniques utilized multiple attribute prompts to align the generated text with desired attributes, but this approach required prompt design for each attribute and was dependent on the size of the language model. This paper introduces a novel method for flexible attribute control in text generation using pre-trained language models (PLMs). The proposed approach aims to enhance the fluency of generated text by guiding the generation process with PPCs. The key idea is to dynamically adjust the distribution of generated text by modifying prompts, effectively constraining the output space of the language model and influencing the desired attribute. To enable smooth cooperation between the PLM and the PPC, our work innovatively proposes a new model fine-tuning method: Reinforcement Learning with Dynamic Adjust Feedback (RLDAF).This fine-tuning process adapts a small subset of the language model's parameters based on the generating actions taken during the PPC control process. The resulting harmonious collaboration between the PLM and PPC leads to improved smoothness in text generation during inference. Extensive experiments were conducted on the SST2 dataset, and the proposed method outperformed previous approaches in various evaluation metrics, including text fluency and attribute consistency.Comment: The Third Version of the Generation, Evaluation & Metrics (GEM) Workshop in EMNLP 202

Multi-type Disentanglement without Adversarial Training

Controlling the style of natural language by disentangling the latent space is an important step towards interpretable machine learning. After the latent space is disentangled, the style of a sentence can be transformed by tuning the style representation without affecting other features of the sentence. Previous works usually use adversarial training to guarantee that disentangled vectors do not affect each other. However, adversarial methods are difficult to train. Especially when there are multiple features (e.g., sentiment, or tense, which we call style types in this paper), each feature requires a separate discriminator for extracting a disentangled style vector corresponding to that feature. In this paper, we propose a unified distribution-controlling method, which provides each specific style value (the value of style types, e.g., positive sentiment, or past tense) with a unique representation. This method contributes a solid theoretical basis to avoid adversarial training in multi-type disentanglement. We also propose multiple loss functions to achieve a style-content disentanglement as well as a disentanglement among multiple style types. In addition, we observe that if two different style types always have some specific style values that occur together in the dataset, they will affect each other when transferring the style values. We call this phenomenon training bias, and we propose a loss function to alleviate such training bias while disentangling multiple types. We conduct experiments on two datasets (Yelp service reviews and Amazon product reviews) to evaluate the style-disentangling effect and the unsupervised style transfer performance on two style types: sentiment and tense. The experimental results show the effectiveness of our model

Updated insights into 3D architecture electrodes for micropower sources

Microbatteries (MBs) and microsupercapacitors (MSCs) are primary on-chip micropower sources that drive autonomous and stand-alone microelectronic devices for implementation of the Internet of Things (IoT). However, the performance of conventional MBs and MSCs is restricted by their 2D thin-film electrode design, and these devices struggle to satisfy the increasing IoT energy demands for high energy density, high power density, and long lifespan. The energy densities of MBs and MSCs can be improved significantly through adoption of a 2D thick-film electrode design; however, their power densities and lifespans deteriorate with increased electrode thickness. In contrast, 3D architecture electrodes offer remarkable opportunities to simultaneously improve MB and MSC energy density, power density, and lifespan. To date, various 3D architecture electrodes have been designed, fabricated, and investigated for MBs and MSCs. This review provides an update on the principal superiorities of 3D architecture electrodes over 2D thick-film electrodes in the context of improved MB and MSC energy density, power density, and lifespan. In addition, the most recent and representative progress in 3D architecture electrode development for MBs and MSCs is highlighted. Finally, present challenges are discussed and key perspectives for future research in this field are outlined

DETERMINANTS OF AGRICULTURE-RELATED LOAN DEFAULT: EVIDENCE FROM CHINA

This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending.This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending

Order-Planning Neural Text Generation From Structured Data

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches do not model the order of contents during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In a biography, for example, the nationality of a person is typically mentioned before occupation in a biography. In this paper, we propose an order-planning text generation model to capture the relationship between different fields and use such relationship to make the generated text more fluent and smooth. We conducted experiments on the WikiBio dataset and achieve significantly higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores

Self-assembly of noble metal nanoparticles into sub-100 nm colloidosomes with collective optical and catalytic properties.

Self-assembly at the nanoscale represents a powerful tool for creating materials with new structures and intriguing collective properties. Here, we report a novel strategy to synthesize nanoscale colloidosomes of noble metals by assembling primary metal nanoparticles at the interface of emulsion droplets formed by their capping agent. This strategy produces noble metal colloidosomes of unprecedentedly small sizes (<100 nm) in high yield and uniformity, which is highly desirable for practical applications. In addition, it enables the high tunability of the composition, producing a diversity of monometallic and bimetallic alloy colloidosomes. The colloidosomes exhibit interesting collective properties that are different from those of individual colloidal nanoparticles. Specifically, we demonstrate Au colloidosomes with well-controlled interparticle plasmon coupling and Au-Pd alloy colloidosomes with superior electrocatalytic performance, both thanks to the special structural features that arise from the assembly. We believe this strategy provides a general platform for producing a rich class of miniature colloidosomes that may have fascinating collective properties for a broad range of applications