activity
20202026
most citedContext Tuning for Retrieval Augmented Generation

4 citations · 5 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.CL2020

A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering

Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu +1

The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriti…

cs.IR2020

Open-Domain Question Answering Goes Conversational via Question Rewriting

Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu +3

We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find a…

cs.LG2020

Generalized Reinforcement Meta Learning for Few-Shot Optimization

Raviteja Anantha, Stephen Pulman, Srinivas Chappidi

We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. During training, it learns the best optimization a…

cs.LG2020

Learning to Rank Intents in Voice Assistants

Raviteja Anantha, Srinivas Chappidi, William Dawoodi

Voice Assistants aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub…

cs.IR2020

Question Rewriting for Conversational Question Answering

Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu +1

Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task b…