306 citations · 1.3k across the 118 of their papers we have counts for
12 papers · 1 filter
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Bosheng Ding, Linlin Liu, Lidong Bing +5
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high q…
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…
Partially-Aligned Data-to-Text Generation with Distant Supervision
Zihao Fu, Bei Shi, Wai Lam +2
The Data-to-Text task aims to generate human-readable text for describing some given structured data enabling more interpretability. However, the typical generation task is confine…
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond
Xin Li, Lidong Bing, Wenxuan Zhang +2
Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach, which have…
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
Lu Xu, Lidong Bing, Wei Lu +1
Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to…
Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Lu Xu, Hao Li, Wei Lu +1
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the se…
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang, Ruidan He, Zuozhu Liu +2
BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence…
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
Hai Ye, Qingyu Tan, Ruidan He +3
Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investig…
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
Juntao Li, Chang Liu, Jian Wang +5
With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumer…
Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization
Piji Li, Lidong Bing, Zhongyu Wei +1
Attention mechanism plays a dominant role in the sequence generation models and has been used to improve the performance of machine translation and abstractive text summarization.…
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Liying Cheng, Dekun Wu, Lidong Bing +4
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language descriptio…
GRET: Global Representation Enhanced Transformer
Rongxiang Weng, Haoran Wei, Shujian Huang +4
Transformer, based on the encoder-decoder framework, has achieved state-of-the-art performance on several natural language generation tasks. The encoder maps the words in the input…