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20182022
most citedSupervised Transfer Learning for Product Information Question Answering

19 citations · 79 across the 25 of their papers we have counts for

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28 papers · 1 filter

cs.CL20221 cited

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Khalil Mrini, Harpreet Singh, Franck Dernoncourt +5

Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs).…

cs.CL2022

Factual Error Correction for Abstractive Summaries Using Entity Retrieval

Hwanhee Lee, Cheoneum Park, Seunghyun Yoon +4

Despite the recent advancements in abstractive summarization systems leveraged from large-scale datasets and pre-trained language models, the factual correctness of the summary is…

cs.CL2022

CAISE: Conversational Agent for Image Search and Editing

Hyounghun Kim, Doo Soon Kim, Seunghyun Yoon +3

Demand for image editing has been increasing as users' desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools re…

cs.CL2021

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

Jianguo Zhang, Trung Bui, Seunghyun Yoon +6

In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-…

cs.CL2021

StreamHover: Livestream Transcript Summarization and Annotation

Sangwoo Cho, Franck Dernoncourt, Tim Ganter +7

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into…

cs.CL20211 cited

UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt +2

Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversit…