1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National University of SingaporeSG55 papers
- Nanyang Technological UniversitySG12 papers
- Beijing Normal UniversityCN7 papers
- Centre for Quantum TechnologiesSG5 papers
- East China Normal UniversityCN4 papers
- Nicolaus Copernicus Astronomical CenterPL4 papers
- Polish Academy of SciencesPL4 papers
- Princeton UniversityUS4 papers
- Arizona State UniversityUS3 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di MilanoIT3 papers
- The Abdus Salam International Centre for Theoretical Physics (ICTP)IT3 papers
- University of InsubriaIT3 papers
5 papers · 1 filter
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
Yerbolat Khassanov, Zhiping Zeng, Van Tung Pham +2
The neural language models (NLM) achieve strong generalization capability by learning the dense representation of words and using them to estimate probability distribution function…
On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
Zhiping Zeng, Yerbolat Khassanov, Van Tung Pham +3
Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances. In this work, we study end-to-end…
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition
Pengcheng Guo, Haihua Xu, Lei Xie +1
In this paper, we present our overall efforts to improve the performance of a code-switching speech recognition system using semi-supervised training methods from lexicon learning…
A Deep Learning Approach for Multimodal Deception Detection
Gangeshwar Krishnamurthy, Navonil Majumder, Soujanya Poria +1
Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet t…
Recent Trends in Deep Learning Based Natural Language Processing
Tom Young, Devamanyu Hazarika, Soujanya Poria +1
Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variet…