A Unified Model for Reverse Dictionary and Definition Modelling

We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model's outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning

Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions

This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks' best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading

To Adapt or to Fine-tune: A Case Study on Abstractive Summarization

Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a massive overhead. Researchers have proposed a few lightweight alternatives such as smaller adapters to mitigate the drawbacks. Nonetheless, it remains uncertain whether using adapters benefits the task of summarization, in terms of improved efficiency without an unpleasant sacrifice in performance. In this work, we carry out multifaceted investigations on fine-tuning and adapters for summarization tasks with varying complexity: language, domain, and task transfer. In our experiments, fine-tuning a pre-trained language model generally attains a better performance than using adapters; the performance gap positively correlates with the amount of training data used. Notably, adapters exceed fine-tuning under extremely low-resource conditions. We further provide insights on multilinguality, model convergence, and robustness, hoping to shed light on the pragmatic choice of fine-tuning or adapters in abstractive summarization

PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India

This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. We detail our construction workflow including data acquisition, processing, and quality assurance. Furthermore, we publish benchmarks for monolingual, cross-lingual, and multilingual summarization by fine-tuning, prompting, as well as translate-and-summarize. Experimental results confirm the crucial role of our data in aiding summarization between Indian languages. Our dataset is publicly available and can be freely modified and re-distributed

2D2D HILIC-ELSD/UPLC-Q-TOF-MS Method for Acquiring Phospholipid Profiles and the Application in Caenorhabditis elegans

Phospholipids are the main constituent of cellular membranes and have recently been identified to have diagnostic value as biomarkers for many diseases. Accordingly, much emphasis is now laid on developing optimal analytical techniques for the phospholipid profiles of various biological samples. In the present study, different classes of phospholipids are first separated by optimized hydrophilic interaction chromatography with evaporative light scattering detector (HILIC-ELSD). The phospholipids in each class are then identified by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Validation results confirm that this approach meets the requirements of quantitative analysis. Finally, the approach is adopted to analyze the phospholipid profiles in Caenorhabditis elegans. A total of 111 phospholipid species is identified according to the mass fragments. Major fatty acyl chains in phospholipids are found to be formed by oleic acid (C18:1), arachidonic acid (C20:4), and eicosapentaenoic acid (C20:5). Overall, this study improves current knowledge on analytical techniques of the phospholipid composition in C. elegans and provides a basis for future lipidomics research. Practical applications: Phospholipids reportedly play a crucial role in the development of many diseases. Until now, only a small portion of phospholipids in Caenorhabditis elegans has been reported by using one-dimensional analysis strategy. The offline 2D2D liquid chromatography method developed in this study identifies 111 phospholipid species in Caenorhabditis elegans. The obtained phospholipid profiles complement the lipid database of Caenorhabditis elegans. The study also provides the basis for the future development of a 2D online approach

Bioinspired Young's Modulus‐Hierarchical E‐Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

Abstract As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature‐pressure electronic skin with decoupling capability (TPD e‐skin), inspired by the high‐low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual‐state amplifying microstructure and contact resistance modulation, the MXene TPD e‐skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high‐low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature‐pressure signals into three types of nerve‐acceptable frequency signals, corresponding to thermoreceptors, slow‐adapting receptors, and fast‐adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain‐like machine learning‐based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality‐decoupling sensing and deep neural integration