14 citations · 21 across the 14 of their papers we have counts for
7 papers · 1 filter
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Arjun Vaithilingam Sudhakar, Prasanna Parthasarathi, Janarthanan Rajendran +1
Large Language Models (LLMs) have demonstrated superior performance in language understanding benchmarks. CALM, a popular approach, leverages linguistic priors of LLMs -- GPT-2 --…
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Singh
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and t…
Quantifying the Effects of COVID-19 on Mental Health Support Forums
Laura Biester, Katie Matton, Janarthanan Rajendran +2
The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategie…
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
Janarthanan Rajendran, Jatin Ganhotra, Lazaros Polymenakos
Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their ina…
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh +1
In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumptio…
NE-Table: A Neural key-value table for Named Entities
Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo +3
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the…