13 citations · 17 across the 8 of their papers we have counts for
8 papers
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
Taxi1500: A Multilingual Dataset for Text Classification in 1500 Languages
Chunlan Ma, Ayyoob ImaniGooghari, Haotian Ye +3
While natural language processing tools have been developed extensively for some of the world's languages, a significant portion of the world's over 7000 languages are still neglec…
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
Yongkang Liu, Shi Feng, Daling Wang +2
We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the las…
Graph Neural Networks for Multiparallel Word Alignment
Ayyoob Imani, Lütfi Kerem Şenel, Masoud Jalili Sabet +2
After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection, and m…
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
Mengjie Zhao, Tao Lin, Fei Mi +2
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. E…
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
Masoud Jalili Sabet, Philipp Dufter, François Yvon +1
Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do me…
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
Martin Schmitt, Sahand Sharifzadeh, Volker Tresp +1
Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semanti…
Embedding Learning Through Multilingual Concept Induction
Philipp Dufter, Mengjie Zhao, Martin Schmitt +2
We present a new method for estimating vector space representations of words: embedding learning by concept induction. We test this method on a highly parallel corpus and learn sem…