Papers by Ella Neeman

2 papers
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering (2023.acl-long)

Copied to clipboard

Challenge: Question answering models have access to two sources of knowledge during inference time: parametric knowledge and contextual knowledge.
Approach: They propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge.
Outcome: The proposed model generates two answers for a given question based on parametric and contextual knowledge.
Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021.emnlp-main)

Copied to clipboard

Challenge: Existing evaluation methods for factual consistency in knowledge-grounded dialogues are unreliable and limit their applicability.
Approach: They propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering.
Outcome: The proposed evaluation metric consistently shows higher correlation with human judgements.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations