7 papers
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces
Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsit…
TopicBERT for Energy Efficient Document Classification
Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena +3
Prior research notes that BERT's computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissio…
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
Yatin Chaudhary, Pankaj Gupta, Hinrich Schütze
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad…
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
Yatin Chaudhary, Pankaj Gupta, Thomas Runkler
Topic models such as LDA, DocNADE, iDocNADEe have been popular in document analysis. However, the traditional topic models have several limitations including: (1) Bag-of-words (BoW…
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsi…
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner +1
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structur…