Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Diversity-Promoting Objective Function for Neural Conversation Models
20161.1k citationsJiwei Li, Michel Galley et al.profile →
Deep Reinforcement Learning for Dialogue Generation
2016610 citationsJiwei Li, Michel Galley et al.profile →
A Persona-Based Neural Conversation Model
2016481 citationsJiwei Li, Michel Galley et al.profile →
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
2015432 citationsMichel Galley, Michael Auli et al.profile →
Citations per year, relative to Michel Galley Michel Galley (= 1×)
peers
Douwe Kiela
Countries citing papers authored by Michel Galley
Since
Specialization
Citations
This map shows the geographic impact of Michel Galley's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michel Galley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michel Galley more than expected).
This network shows the impact of papers produced by Michel Galley. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michel Galley. The network helps show where Michel Galley may publish in the future.
Co-authorship network of co-authors of Michel Galley
This figure shows the co-authorship network connecting the top 25 collaborators of Michel Galley.
A scholar is included among the top collaborators of Michel Galley based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Michel Galley. Michel Galley is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Galley, Michel, Baolin Peng, Weixin Cai, et al.. (2023). Interactive Text Generation. 4450–4468.1 indexed citations
2.
Zhang, Yizhe, Xiang Gao, Sung‐Jin Lee, et al.. (2021). IMPROVING RESPONSE GENERATION CONSISTENCY VIA CONTRASTIVE LEARNING. Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Gao, Jianfeng, Michel Galley, & Lihong Li. (2018). Neural Approaches to Conversational AI – Tutorial at ACL/SIGIR 2018. International ACM SIGIR Conference on Research and Development in Information Retrieval.2 indexed citations
5.
Mostafazadeh, Nasrin, Chris Brockett, Bill Dolan, et al.. (2017). Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation. arXiv (Cornell University). 1. 462–472.13 indexed citations
6.
Luan, Yi, Chris Brockett, Bill Dolan, Jianfeng Gao, & Michel Galley. (2017). Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models. International Joint Conference on Natural Language Processing. 1. 605–614.11 indexed citations
7.
Huang, Ting-Hao, Francis Ferraro, Nasrin Mostafazadeh, et al.. (2016). Visual Storytelling. 1233–1239.138 indexed citations
8.
Li, Jiwei, Michel Galley, Chris Brockett, et al.. (2016). A Persona-Based Neural Conversation Model. 994–1003.481 indexed citations breakdown →
9.
Ferraro, Francis, Nasrin Mostafazadeh, Ting-Hao Huang, et al.. (2015). On Available Corpora for Empirical Methods in Vision & Language.. arXiv (Cornell University).1 indexed citations
Chung, Tagyoung & Michel Galley. (2012). Direct Error Rate Minimization for Statistical Machine Translation. Workshop on Statistical Machine Translation. 468–479.5 indexed citations
13.
Galley, Michel & Chris Quirk. (2011). Optimal Search for Minimum Error Rate Training. Empirical Methods in Natural Language Processing. 38–49.10 indexed citations
14.
Toutanova, Kristina & Michel Galley. (2011). Why Initialization Matters for IBM Model 1: Multiple Optima and Non-Strict Convexity. Meeting of the Association for Computational Linguistics. 461–466.11 indexed citations
15.
Galley, Michel & Christopher D. Manning. (2010). Accurate Non-Hierarchical Phrase-Based Translation. North American Chapter of the Association for Computational Linguistics. 966–974.41 indexed citations
16.
Cer, Daniel, Michel Galley, Daniel Jurafsky, & Christopher D. Manning. (2010). Phrasal: a toolkit for statistical machine translation with facilities for extraction and incorporation of arbitrary model features. North American Chapter of the Association for Computational Linguistics. 9–12.25 indexed citations
17.
Green, Spence, Michel Galley, & Christopher D. Manning. (2010). Improved Models of Distortion Cost for Statistical Machine Translation. North American Chapter of the Association for Computational Linguistics. 867–875.21 indexed citations
Padó, Sebastian, Michel Galley, Daniel Jurafsky, & Christopher D. Manning. (2009). Machine Translation Evaluation with Textual Entailment Features. Workshop on Statistical Machine Translation. 37–41.7 indexed citations
20.
McKeown, Kathleen, Julia Hirschberg, Michel Galley, & Sameer Maskey. (2005). From Text Summarization to Speech Summarization. International Conference on Acoustics, Speech, and Signal Processing.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.