Search Results for author:
Found 7 papers, 2 papers with code
What BERT Based Language Model Learns in Spoken Transcripts: An Empirical Study
We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues.
Improving Pinterest Search Relevance Using Large Language Models
To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to predict the relevance of Pins effectively.
What BERT Based Language Models Learn in Spoken Transcripts: An Empirical Study
We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues.
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript
In this work, we propose a BERT-style language model, referred to as PhonemeBERT, that learns a joint language model with phoneme sequence and ASR transcript to learn phonetic-aware representations that are robust to ASR errors.
Unsupervised Aspect-Level Sentiment Controllable Style Transfer
Unsupervised style transfer in text has previously been explored through the sentiment transfer task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Sentiment-Aware Recommendation System for Healthcare using Social Media
Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community.
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm.
Ranked #8 on
Crowd Counting
on UCF CC 50
