Titlebook: Chinese Computational Linguistics; 21st China National Maosong Sun,Yang Liu,Yubo Chen Conference proceedings 2022 The Editor(s) (if applic

招募

Fundamental organization models,ations in natural language processing tasks such as reading comprehension, question and answer systems. The main approach is to compute the interaction between text representations and sentence pairs through an attention mechanism, which can extract the semantic information between sentence pairs we

engrossed

ORBIT

Markus F. Peschl,Thomas Fundneiderabeled data. In this paper, we propose a data synthesis and iterative refinement framework for neural semantic parsing, which can build semantic parsers without annotated logical forms. We first generate a naive corpus by sampling logic forms from knowledge bases and synthesizing their canonical utt

Osteoporosis

Fracture

Management – Culture – Interpretation, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the

BIDE

vertebrate

Markus F. Peschl,Thomas Fundneidersing emotion. Humans express not only their emotional state but also the stimulus that caused the emotion, i.e., emotion cause, during a conversation. Most existing approaches focus on emotion modeling, emotion recognition and prediction, and emotion fusion generation, ignoring the critical aspect o

bourgeois

https://doi.org/10.1007/978-94-6300-821-1odels 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

河流

Joseph Livesey,Dominik Wojtczakical question answering, and automatic medical record analysis, etc. Compared with named entities (NEs) in general domain, medical named entities are usually more complex and prone to be nested. To cope with both flat NEs and nested NEs, we propose a MRC-based approach with multi-task learning and m

atopic-rhinitis

Paul C. Bell,Patrick Totzke,Igor Potapovnt decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge difficulty. We propose a multi-gate encoder that models bidirectional task interaction while keeping sufficient feature specificity bas