Demo Data from EDBT 2026 / reviewers in the wild / expert
Yanzhuo Ding
dblp:202/1982
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 67% Machine translation · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
interpretability |
0.3 | 1 | 2017 | Visualizing and Understanding Neural Machine Translation · ACL (1) 2017 |
Machine learning › Trustworthy machine learning › interpretability › attribution methods
layer-wise relevance propagation |
0.3 | 1 | 2017 | Visualizing and Understanding Neural Machine Translation · ACL (1) 2017 |
Natural language and speech › Machine translation
neural machine translation |
0.3 | 1 | 2017 | Visualizing and Understanding Neural Machine Translation · ACL (1) 2017 |
Methods — techniques the papers use, named apart from their topics
layer-wise relevance propagation · 0.3attention visualization · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Visualizing and Understanding Neural Machine TranslationabstractWhile neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks.In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoderdecoder framework.We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors. Yanzhuo Ding, Yang Liu 0005, Huan-Bo Luan, Maosong Sun 0001 |
ACL (1) | 1 |