1 code implementation • 3 Mar 2024 • Heegon Jin, Seonil Son, Jemin Park, Youngseok Kim, Hyungjong Noh, Yeonsoo Lee
The Attention Alignment Module in A2D performs a dense head-by-head comparison between student and teacher attention heads across layers, turning the combinatorial mapping heuristics into a learning problem.
2 code implementations • 22 Nov 2022 • Seonil Son, Junsoo Park, Jeong-in Hwang, Junghwa Lee, Hyungjong Noh, Yeonsoo Lee
One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text.
no code implementations • arXiv 2021 • Sanghyuk Choi, Jeong-in Hwang, Hyungjong Noh, Yeonsoo Lee
Recent neural sequence-to-sequence models with a copy mechanism have achieved remarkable progress in various text generation tasks.
no code implementations • 13 Sep 2019 • Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, Jaehyo Yoo, Bum Chul Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
no code implementations • 20 Jul 2018 • Minjeong Kim, David Keetae Park, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo
Machine comprehension question answering, which finds an answer to the question given a passage, involves high-level reasoning processes of understanding and tracking the relevant contents across various semantic units such as words, phrases, and sentences in a document.