156 citations
- Amazon (United States)US12 papers
- Massachusetts Institute of TechnologyUS7 papers
- Carnegie Mellon UniversityUS6 papers
- Google (United States)US5 papers
- Johns Hopkins UniversityUS5 papers
- Meta (Israel)IL5 papers
- California Southern UniversityUS4 papers
- Microsoft Research (United Kingdom)GB4 papers
- Toyota Technological Institute at ChicagoUS4 papers
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- University of Southern CaliforniaUS4 papers
- National Yang Ming Chiao Tung UniversityTW3 papers
31 papers · 2 filters
Supervised Graph Contrastive Pretraining for Text Classification
Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related tasks which share labe…
FANS: Fusing ASR and NLU for on-device SLU
Martin Radfar, Athanasios Mouchtaris, Siegfried Kunzmann +1
Spoken language understanding (SLU) systems translate voice input commands to semantics which are encoded as an intent and pairs of slot tags and values. Most current SLU systems d…
Training Conversational Agents with Generative Conversational Networks
Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +1
Rich, open-domain textual data available on the web resulted in great advancements for language processing. However, while that data may be suitable for language processing tasks,…
Multiplicative Position-aware Transformer Models for Language Understanding
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks. The self-attention mechanism…
Faithful Target Attribute Prediction in Neural Machine Translation
Xing Niu, Georgiana Dinu, Prashant Mathur +1
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predict…
Towards Continual Entity Learning in Language Models for Conversational Agents
Ravi Teja Gadde, Ivan Bulyko
Neural language models (LM) trained on diverse corpora are known to work well on previously seen entities, however, updating these models with dynamically changing entities such as…