Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning

Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper we investigate the challenging task of less-data constrained generation, especially when the generated news headlines are short yet expected by readers to keep readable and informative simultaneously. We highlight the key information modeling task and propose a novel duality fine-tuning method by formally defining the probabilistic duality constraints between key information prediction and headline generation tasks. The proposed method can capture more information from limited data, build connections between separate tasks, and is suitable for less-data constrained generation tasks. Furthermore, the method can leverage various pre-trained generative regimes, e.g., autoregressive and encoder-decoder models. We conduct extensive experiments to demonstrate that our method is effective and efficient to achieve improved performance in terms of language modeling metric and informativeness correctness metric on two public datasets.Comment: Accepted by AACL-IJCNLP 2022 main conferenc

Application of keyword extraction on MOOC resources

Purpose – Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well. Design/methodology/approach – Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels. Findings – From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform. Research limitations/implications – onducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required. Practical implications – The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved. Originality/value – This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of great significance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE). More specifically, this framework features a novel and tailored transformer, named as Relation-augmented Attention Transformer (RAAT). RAAT is scalable to capture multi-scale and multi-amount argument relations. To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance. Extensive experiments demonstrate the effectiveness of the proposed method, which can achieve state-of-the-art performance on two public datasets. Our code is available at https://github. com/TencentYoutuResearch/RAAT.Comment: Accepted by NAACL 202

A Low-Power, Fast-Transient Output-Capacitorless LDO with Transient Enhancement Unit and Current Booster

With the wide application of advanced portable devices, output-capacitorless low dropout regulators (OCL-LDO) are receiving increasing attention. This paper presents a low quiescent current OCL-LDO with fast transient response. A transient enhancement unit (TEU) is proposed as the output voltage-spike detection circuit. It enhances the transient response by improving the slew-rate at the gate of the power transistor. In addition, a current booster (CB), which consists of a current subtractor and a non-linear current mirror, is designed to improve the slew-rate further. The current subtractor increases the transconductances of the differential-input transistors to obtain a large slewing current, while the non-linear current mirror further boosts the current with no extra quiescent current consumption. The simulated results show that the proposed OCL-LDO is capable of supplying 100 mA load current while consuming 10.3 μA quiescent current. It regulates the output at 1 V from a supply voltage ranging from 1.2 to 1.8 V. When the load current is stepped from 1 mA to 100 mA in 100 ns, the OCL-LDO has attained a settling time of 190 ns, and the output voltage undershoot and overshoot are controlled under 110 mV

Digestion of Alumina from Non-Magnetic Material Obtained from Magnetic Separation of Reduced Iron-Rich Diasporic Bauxite with Sodium Salts

Recovery of iron from iron-rich diasporic bauxite ore via reductive roasting followed by magnetic separation has been explored recently. However, the efficiency of alumina extraction in the non-magnetic materials is absent. In this paper, a further study on the digestion of alumina by the Bayer process from non-magnetic material obtained after magnetic separation of reduced iron-rich diasporic bauxite with sodium salts was investigated. The results indicate that the addition of sodium salts can destroy the original occurrences of iron-, aluminum- and silicon-containing minerals of bauxite ore during reductive roasting. Meanwhile, the reactions of sodium salts with complex aluminum- and silicon-bearing phases generate diaoyudaoite and sodium aluminosilicate. The separation of iron via reductive roasting of bauxite ore with sodium salts followed by magnetic separation improves alumina digestion in the Bayer process. When the alumina-bearing material in bauxite ore is converted into non-magnetic material, the digestion temperature decreases significantly from 280 °C to 240 °C with a nearly 99% relative digestion ratio of alumina

A Low-Power, Fast-Transient Output-Capacitorless LDO with Transient Enhancement Unit and Current Booster

With the wide application of advanced portable devices, output-capacitorless low dropout regulators (OCL-LDO) are receiving increasing attention. This paper presents a low quiescent current OCL-LDO with fast transient response. A transient enhancement unit (TEU) is proposed as the output voltage-spike detection circuit. It enhances the transient response by improving the slew-rate at the gate of the power transistor. In addition, a current booster (CB), which consists of a current subtractor and a non-linear current mirror, is designed to improve the slew-rate further. The current subtractor increases the transconductances of the differential-input transistors to obtain a large slewing current, while the non-linear current mirror further boosts the current with no extra quiescent current consumption. The simulated results show that the proposed OCL-LDO is capable of supplying 100 mA load current while consuming 10.3 μA quiescent current. It regulates the output at 1 V from a supply voltage ranging from 1.2 to 1.8 V. When the load current is stepped from 1 mA to 100 mA in 100 ns, the OCL-LDO has attained a settling time of 190 ns, and the output voltage undershoot and overshoot are controlled under 110 mV

A General Planning-Based Framework for Goal-Driven Conversation Assistant

We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains