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.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
2015466 citationsTsung-Hsien Wen, Milica Gašić et al.profile →
POMDP-Based Statistical Spoken Dialog Systems: A Review
2013460 citationsSteve Young, Milica Gašić et al.profile →
A Network-based End-to-End Trainable Task-oriented Dialogue System
2017451 citationsTsung-Hsien Wen, David Vandyke et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Milica Gašić'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 Milica Gašić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milica Gašić more than expected).
This network shows the impact of papers produced by Milica Gašić. 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 Milica Gašić. The network helps show where Milica Gašić may publish in the future.
Co-authorship network of co-authors of Milica Gašić
This figure shows the co-authorship network connecting the top 25 collaborators of Milica Gašić.
A scholar is included among the top collaborators of Milica Gašić 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 Milica Gašić. Milica Gašić is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rojas-Barahona, Lina M., Milica Gašić, Nikola Mrkšić, et al.. (2016). Exploiting sentence and context representations in deep neural models for spoken language understanding. Cambridge University Engineering Department Publications Database.7 indexed citations
Wen, Tsung-Hsien, Milica Gašić, Nikola Mrkšić, et al.. (2015). Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. 1711–1721.466 indexed citations breakdown →
13.
Gašić, Milica, Catherine Breslin, Matthew Henderson, et al.. (2013). POMDP-based dialogue manager adaptation to extended domains. Cambridge University Engineering Department Publications Database. 214–222.35 indexed citations
14.
Hastie, Helen, Marie-Aude Aufaure, Heriberto Cuayáhuitl, et al.. (2013). Demonstration of the PARLANCE system: a data-driven incremental, spoken dialogue system for interactive search. 154–156.17 indexed citations
15.
Tsiakoulis, Pirros, Milica Gašić, Matthew Henderson, et al.. (2012). Statistical methods for building robust spoken dialogue systems in an automobile. Cambridge University Engineering Department Publications Database.5 indexed citations
Gašić, Milica, Filip Jurčíček, Simon Keizer, et al.. (2010). Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers. Cambridge University Engineering Department Publications Database. 201–204.38 indexed citations
19.
Keizer, Simon, Milica Gašić, Filip Jurčíček, et al.. (2010). Parameter estimation for agenda-based user simulation. Cambridge University Engineering Department Publications Database. 116–123.24 indexed citations
20.
Mairesse, François, Milica Gašić, Filip Jurčíček, et al.. (2010). Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning. Cambridge University Engineering Department Publications Database. 1552–1561.73 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.