--- title: "Jekaterina Novikova — author profile and papers" description: "Jekaterina Novikova, Vanguard: 2 papers on Elamur Research (2017), h-index 18 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/jekaterina-novikova/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jekaterina-novikova/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jekaterina Novikova # Jekaterina Novikova Jekaterina Novikova works on natural language generation, especially systems that turn structured meaning representations into fluent text for dialogue applications. Their papers here ask what data such systems should learn from and how their output should be judged. The work relies on crowdsourced corpora in domains such as restaurants and hotels, and compares automatic measures of output quality with ratings collected from people. These concerns sit within the topics of [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/) and [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/). The most cited of these papers, both from 2017, are the natural place to start. [The E2E Dataset: New Challenges For End-to-End Generation](https://md.research.elamur.ai/free/en/papers/novikova-2017-e2e-dataset-new-challenges-end/) presents a large crowdsourced corpus that pairs dialogue-act-style meaning representations with restaurant descriptions written by people. Earlier end-to-end generators had mostly been trained on small delexicalised collections that yielded rather formulaic sentences. To elicit more natural wording, crowd workers saw pictures rather than textual prompts, and the new corpus is contrasted with older ones such as BAGEL. The companion study, [Why We Need New Evaluation Metrics for NLG](https://md.research.elamur.ai/free/en/papers/novikova-2017-why-we-need-new-evaluation/), tests how closely word-overlap, semantic-similarity and grammar-based metrics such as BLEU track human judgements. It runs these tests on the output of several data-driven generators across restaurant and hotel corpora, with particular attention to systems that can produce ungrammatical text. Read together, the two papers examine both the training material for data-driven generation and the methods used to assess it. Novikova is currently at Vanguard. Earlier bylines include [Heriot-Watt University](https://md.research.elamur.ai/free/en/universities/heriot-watt-university/), where much of the work on generation and evaluation appeared, as well as the University of Bath and, more recently, [Cohere](https://md.research.elamur.ai/free/en/companies/cohere/). Co-authors on the papers here include [Ondřej Dušek](https://md.research.elamur.ai/free/en/authors/ondrej-dusek/) and [Verena Rieser](https://md.research.elamur.ai/free/en/authors/verena-rieser/), with [Amanda Cercas Curry](https://md.research.elamur.ai/free/en/authors/amanda-cercas-curry/) also appearing. Jekaterina Novikova has 2 papers in our library, in 2017, most of them on [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Ondřej Dušek](https://md.research.elamur.ai/free/en/authors/ondrej-dusek/), [Verena Rieser](https://md.research.elamur.ai/free/en/authors/verena-rieser/), [Amanda Cercas Curry](https://md.research.elamur.ai/free/en/authors/amanda-cercas-curry/). 2 papers in our library · 2017 [OpenAlex](https://openalex.org/A5050825493) · [ORCID](https://orcid.org/0000-0003-4754-6126) · [**h-index 18**](https://openalex.org/authors/A5050825493) · [68 works](https://openalex.org/works?filter=author.id:A5050825493) · [3,192 citations](https://openalex.org/works?filter=author.id:A5050825493&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/jekaterina-novikova/#papers) · [🔗 1,819 citations in our library](https://md.research.elamur.ai/free/en/authors/jekaterina-novikova/cited-by/) · [3 more in our library without a page yet](#also) · [⭐ Paper Star rank #2381](https://md.research.elamur.ai/free/en/paper-stars/page/24/#star-2381) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Jekaterina+Novikova) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jekaterina+Novikova) · [dblp](https://dblp.org/search?q=Jekaterina+Novikova) · [ACL Anthology](https://aclanthology.org/search/?q=Jekaterina+Novikova) ## Affiliations [🏢**Vanguard** Valley Forge 2021–2026 · 4 papers*current*](https://ror.org/032kzxf45) · [🎓**Heriot-Watt University** Edinburgh · United Kingdom 2016–2021 · 16 papers](https://md.research.elamur.ai/free/en/universities/heriot-watt-university/) · [🎓**University of Bath** Bath · United Kingdom 2014–2015 · 10 papers](https://ror.org/002h8g185) · [🏢**Cohere** San Francisco 2024–2025 · 2 papers](https://md.research.elamur.ai/free/en/companies/cohere/) · [🎓**Yale University** New Haven · United States 2022 · 2 papers](https://md.research.elamur.ai/free/en/universities/yale-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Text generation (1)](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Evaluation & benchmarks (1)](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) ## Works by year ### 2010s [**2017** 2 papers](https://md.research.elamur.ai/free/en/authors/jekaterina-novikova/year/2017/) ## Papers The most cited first (OpenAlex and our library). 1. **[The E2E Dataset: New Challenges For End-to-End Generation](https://md.research.elamur.ai/free/en/papers/novikova-2017-e2e-dataset-new-challenges-end/)** *(2017 · 461 citations)* End-to-end NLG systems handle sentence planning and surface realisation together, learning directly from unaligned pairs of meaning representations (MRs) and texts. 2. **[Why We Need New Evaluation Metrics for NLG](https://md.research.elamur.ai/free/en/papers/novikova-2017-why-we-need-new-evaluation/)** *(2017 · 287 citations)* The authors examine how well automatic NLG evaluation metrics, such as BLEU, agree with human judgements of a system. ## Also in our library 3 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 3 co-authors with a page, the most papers together first. - [Ondřej Dušek](https://md.research.elamur.ai/free/en/authors/ondrej-dusek/) ×2 - [Verena Rieser](https://md.research.elamur.ai/free/en/authors/verena-rieser/) ×2 - [Amanda Cercas Curry](https://md.research.elamur.ai/free/en/authors/amanda-cercas-curry/) ## Cited by Papers of our library cite their works 1,819 times (of the 140,884 papers whose bibliographies we read); [180 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/jekaterina-novikova/cited-by/).